1. Bibliographic Information

1.1. Title

The central topic of the paper is "Animate3D: Animating Any 3D Model with Multi-view Video Diffusion." It focuses on developing a framework to animate static 3D models using a novel multi-view video diffusion approach.

1.2. Authors

The authors are:

• Yanqin Jiang (State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA; School of Artificial Intelligence, University of Chinese Academy of Sciences)

• Chaohui Yu (DAMO Academy, Alibaba Group; Hupan Lab)

• Chenjie Cao (DAMO Academy, Alibaba Group; Hupan Lab)

• Fan Wang (DAMO Academy, Alibaba Group; Hupan Lab)

• Weiming Hu (State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA; School of Artificial Intelligence, University of Chinese Academy of Sciences; School of Information Science and Technology, ShanghaiTech University)

• Jin Gao (State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA; School of Artificial Intelligence, University of Chinese Academy of Sciences)

  Yanqin Jiang and Chaohui Yu contributed equally. Jin Gao is the corresponding author. Their affiliations indicate a strong presence in academic and industry research institutions, particularly in AI, computer vision, and machine learning, with a focus on artificial intelligence systems and Alibaba Group's DAMO Academy.

1.3. Journal/Conference

The paper was published at OpenReview, with a publication date of 2024-11-06T00:00:00.000Z. OpenReview is a platform primarily used for hosting submissions to major conferences (e.g., NeurIPS, ICLR) during their review process and often for post-publication access to papers and reviews. The publication date suggests it has been officially accepted and published by a conference or journal.

1.4. Publication Year

2024

1.5. Abstract

This paper introduces Animate3D, a novel framework designed to animate any static 3D model. Existing 4D (3D + time) generation methods often struggle with spatiotemporal inconsistency and cannot effectively leverage pre-existing 3D assets with multi-view attributes, as they typically distill from text or single-view image-conditioned models. Animate3D addresses these issues through two main components:

1. A novel multi-view video diffusion model (MV-VDM): This model is conditioned on multi-view renderings of the static 3D object and is trained on a newly presented large-scale dataset called MV-Video. The MV-VDM incorporates a spatiotemporal attention module to enhance both spatial and temporal consistency by integrating 3D and video diffusion models, while also using multi-view renderings to preserve the object's identity.

2. A framework combining reconstruction and 4D Score Distillation Sampling (4D-SDS): This framework leverages the MV-VDM's diffusion priors to animate 3D objects. It employs a two-stage pipeline: first, reconstructing coarse motions from generated multi-view videos, and then refining fine-level motions using 4D-SDS.

   The framework enables straightforward mesh animation due to its accurate motion learning. Qualitative and quantitative experiments demonstrate Animate3D's significant superiority over previous approaches. Data, code, and models are planned for open release.

1.6. Original Source Link

Official Source Link: https://openreview.net/forum?id=HB6KaCFiMN PDF Link: https://openreview.net/pdf?id=HB6KaCFiMN

2. Executive Summary

2.1. Background & Motivation

The core problem the paper aims to solve is the significant challenge in 4D generation, which refers to creating dynamic 3D content (3D objects that move over time). This field faces two primary issues:

1. Spatiotemporal Inconsistency: Existing 4D generation models often struggle to maintain consistent visual appearance and dynamic motion across both space (different views) and time (different frames). This is largely because they tend to distill knowledge from separate pre-trained text-to-image (T2I) or 3D diffusion models for spatial appearance and video diffusion models for temporal motions. This decoupled learning approach leads to an accumulation of appearance degradation as motion changes, as highlighted in Figure 1, where methods like $SVD+Zero123$ show inconsistency.

2. Inability to Animate Existing 3D Assets with Multi-view Attributes: With the rapid development of static 3D generation (creating high-quality static 3D models), there's a growing demand to animate these off-the-shelf 3D assets. However, current 4D modeling techniques, often based on text- or single-view-conditioned models (like Zero123), fail to faithfully preserve the original multi-view attributes and identity of these assets during the animation process. For instance, the back of an object might be ignored or misrepresented if only a front view is used for conditioning, as illustrated by the butterfly example in Figure 1.

   This problem is crucial due to the wide applicability of 3D content creation in industries like Augmented Reality (AR)/Virtual Reality (VR), gaming, and film. The existing gaps prevent the seamless integration of high-quality static 3D content into dynamic scenarios and lead to subpar results.

The paper's entry point and innovative idea revolve around advocating for a unified approach: animating any off-the-shelf 3D models with unified spatiotemporal consistent supervision. This means moving away from separate distillation steps and instead building a foundational 4D generation model that inherently understands both spatial and temporal consistency. This approach aims to directly leverage existing 3D assets and eliminate error accumulation.

2.2. Main Contributions / Findings

The paper makes several significant contributions to the field of 4D generation:

1. First 4D Generation Framework for Any 3D Objects with Multi-view Conditions: The paper introduces Animate3D, presented as the first framework capable of animating any existing static 3D model while faithfully preserving its detailed multi-view attributes. A notable extension is its ability to achieve mesh animation without the need for traditional skeleton rigging.

2. Novel Foundational 4D Generation Model (MV-VDM): The core of Animate3D is the Multi-view Video Diffusion Model (MV-VDM). This model is specifically designed to jointly model spatiotemporal consistency, addressing the limitations of previous methods that struggled with appearance degradation and motion inconsistency. It integrates 3D and video diffusion models and is conditioned on multi-view renderings of the static 3D object.

3. Largest Multi-view Video Dataset (MV-Video): To train the MV-VDM, the authors constructed MV-Video, the largest known 4D dataset. It comprises approximately 84,000 animations, leading to over 1.3 million multi-view videos, which is a critical factor for developing data-driven foundational models in 4D generation.

4. Effective Two-Stage Optimization Pipeline: Based on MV-VDM, the paper proposes an effective pipeline for animating 3D objects. This pipeline combines reconstruction and 4D Score Distillation Sampling (4D-SDS) to optimize 4D Gaussian Splatting (4DGS). It first reconstructs coarse motions from generated multi-view videos and then refines fine-level motions using 4D-SDS, benefiting from the MV-VDM's priors.

   The key findings demonstrate that Animate3D significantly outperforms previous approaches both qualitatively and quantitatively, generating spatiotemporally consistent 4D objects with high-quality appearance and natural motion, making it a highly practical solution for various downstream 4D applications.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To fully grasp the paper's methodology, a reader should understand the following fundamental concepts:

• Diffusion Models: Diffusion models are a class of generative models that have achieved state-of-the-art results in image and video synthesis. They operate by learning to reverse a diffusion process.

  • Forward Diffusion Process: In this process, noise (typically Gaussian noise) is gradually added to a data sample (e.g., an image) over a series of steps, eventually transforming it into pure noise. The amount of noise added at each step is controlled by a variance schedule.
  • Reverse Diffusion Process: This is the generative part. A neural network (often a U-Net) is trained to predict the noise that was added in the forward process or to predict the original clean data from a noisy version. By iteratively subtracting the predicted noise (or predicting the clean data) starting from pure noise, the model can generate new data samples.
  • Latent Diffusion Models (LDMs): Instead of operating directly in pixel space, LDMs perform the diffusion process in a compressed latent space. An encoder maps high-dimensional data (like images) to a lower-dimensional latent representation, and a decoder reconstructs the image from the latent. This makes the diffusion process computationally more efficient while still producing high-quality results.

• 4D Generation (3D + Time): 4D generation refers to the creation of dynamic 3D content. This means generating a 3D object or scene that also changes and moves over a period of time. It combines the challenges of 3D generation (spatial consistency, novel view synthesis) with video generation (temporal consistency, realistic motion).

• 3D Gaussian Splatting (3DGS): 3D Gaussian Splatting (3DGS) is a novel 3D representation technique for real-time radiance field rendering. Instead of representing a scene with voxels, meshes, or neural fields, it uses a collection of 3D Gaussians. Each Gaussian is defined by its position, covariance (which determines its shape and orientation), opacity, and color. These Gaussians can be efficiently rendered by projecting them onto 2D image planes. This representation allows for high-quality, real-time rendering and is often easier to optimize than neural radiance fields (NeRFs). In a 4DGS context, the properties of these Gaussians (e.g., position, rotation, scale) can change over time to represent dynamic scenes.

• Score Distillation Sampling (SDS): Score Distillation Sampling (SDS) is a technique used to optimize 3D representations (like NeRFs or 3DGS) using gradients from pre-trained 2D diffusion models. The core idea is to guide the 3D generation process by minimizing a loss function that encourages the 3D model's rendered 2D views to align with the "score" (the gradient of the log probability density) of a 2D diffusion model. This allows leveraging the rich visual priors learned by large-scale 2D diffusion models to synthesize high-quality 3D content from various prompts (e.g., text, images). The loss essentially tells the 3D model: "Make your rendered image look more like what the 2D diffusion model would generate for this prompt."

• Multi-view Rendering: Multi-view rendering refers to the process of generating images of a 3D object or scene from multiple different camera angles or viewpoints. This is crucial for evaluating and training 3D/4D models, as it allows for assessing consistency and completeness of the 3D representation from all sides.

• Attention Mechanism (Self-attention, Cross-attention): The attention mechanism is a core component in modern deep learning models, especially Transformers. It allows a model to weigh the importance of different parts of the input when processing a sequence or set of data.

  • Self-attention: Relates different positions of a single sequence to compute a representation of the same sequence. For example, in a sentence, it helps a word understand its context by looking at all other words in the sentence.
  • Cross-attention: Relates two different sequences. For instance, in a text-to-image model, it allows the image generation process to pay attention to relevant parts of the text prompt. These mechanisms are fundamental for diffusion models to integrate conditional information (e.g., text prompts, image conditions, camera parameters) into the denoising process.

3.2. Previous Works

The paper frames its contributions by contrasting them with existing approaches in 3D, video, and 4D generation.

3.2.1. 3D Generation

Early efforts in 3D generation often optimized single 3D objects using CLIP loss or Score Distillation Sampling (SDS) from 2D text-to-image (T2I) diffusion models. However, because these supervising models lacked inherent 3D understanding, the generated 3D objects frequently suffered from spatial inconsistency, famously known as the multi-face Janus problem (e.g., an object having multiple faces or distorted features when viewed from different angles).

To address this, two main directions emerged:

• Multi-view Image Diffusion: Works like Zero123, MVDream, and ImageDream lifted T2I diffusion models to multi-view image diffusion. They achieved this by injecting new spatial attention layers and fine-tuning on large-scale 3D synthetic datasets (e.g., Objaverse). While improving 3D consistency, these methods are still optimization-based, requiring relatively long times to optimize a 3D object.
• Feed-forward 3D Generation Foundation Models: More recent approaches such as LRM and GRM trained large reconstruction models from scratch on massive 3D datasets. These models can produce high-quality 3D objects in an inference-only way within seconds, showcasing the power of data-driven methods in 3D generation.

3.2.2. Video Generation

Video generation evolved from text-to-video (T2V) models to image-to-video (I2V) models.

• Text-to-Video (T2V) Generation: Pioneering T2V works (e.g., Imagen Video, Make-A-Video, Stable Video Diffusion, AnimateDiff) typically built upon pre-trained T2I diffusion models. They achieved video generation by keeping the spatial blocks of the T2I model unchanged and inserting new temporal blocks to model camera or object motions over time.
• Image-to-Video (I2V) Generation: Following T2V advancements, I2V methods (e.g., I2V-Adapter) incorporate image semantics into video models. This is usually done through cross-attention mechanisms where noisy frames attend to conditional images, while largely retaining the motion module designs from the T2V models.

3.2.3. 4D Generation

4D generation methods combine aspects of 3D and video generation.

• MAV3D was a pioneering work, proposing a multi-stage pipeline: first optimizing static 3D objects via a T2I model, then learning motions using a T2V model.
• Subsequent works (4Dfy, DreamGaussian4D, Align Your Gaussians, Consistent4D, Animate124, 4DGen) largely followed a similar multi-stage paradigm. They often found T2I and 3D-SDS (from models like Zero123) crucial for both object generation and motion learning, but faced challenges:

  • Appearance Degradation: Without proper supervision, the quality of generated objects could suffer.

  • Motion Learning Failure: The motion learning process was prone to issues.

  • Spatial and Temporal Inconsistency: This is a critical issue. As the paper points out, since the diffusion models used for SDS were not trained on multi-view video (4D) datasets, they lacked the inherent capacity to simultaneously ensure spatial and temporal consistency. This led to difficulties in balancing appearance and motion learning, resulting in issues like small motion amplitude or shaky appearance.

  • Single-view Dependency: Many methods rely on text or single-view-conditioned models (like Zero123) for SDS. Zero123, for example, conditions only on a front view, leading to Novel View Synthesis (NVS) optimizations that favor pre-trained data distributions, potentially causing appearance degradation or inconsistency in novel views (e.g., the blurred back in Figure 3). Monocular video-guided motion learning also suffers from ambiguity (e.g., object moving closer/farther interpreted as size change).

    The paper argues that existing 4D generation methods, by relying on separate 3D and video models, suffer from a fundamental issue: their supervision signals for motion and appearance are not orthogonal and can even conflict. For instance, video-diffusion-SDS can negatively impact appearance, while strong appearance supervision can hinder natural motion learning.

3.3. Technological Evolution

The evolution in this field has progressed from static 2D image generation to static 3D object generation, and now to dynamic 4D content generation.

1. 2D Image Generation: Fueled by large datasets and computational power, Diffusion Models revolutionized text-to-image synthesis.

2. 3D Static Object Generation: Researchers leveraged these powerful 2D priors for 3D generation, initially through SDS (e.g., DreamFusion) and later with multi-view conditioned models (e.g., MVDream, Zero123) or direct data-driven 3D reconstruction models (LRM, GRM). The key challenge here was achieving 3D consistency across different views.

3. Video Generation: Simultaneously, Diffusion Models were extended to video generation by adding temporal modules to existing T2I models. The challenge here was temporal consistency and realistic motion.

4. 4D Dynamic Content Generation: The logical next step was to combine 3D and video capabilities to create dynamic 3D content. However, simply combining existing 3D and video models (distillation) proved insufficient due to the inherent mismatch in their learned priors regarding spatiotemporal consistency.

   This paper's work fits into the latter part of this timeline, attempting to overcome the limitations of the distillation paradigm in 4D generation by proposing a unified 4D foundation model trained on multi-view video data. This represents a shift towards a more holistic approach to 4D modeling.

3.4. Differentiation Analysis

Compared to the main methods in related work, Animate3D introduces several core differences and innovations:

1. Unified Spatiotemporal Consistency:

   • Previous methods: 4Dfy, DG4D, Consistent4D, and others typically distill knowledge from separate pre-trained 3D diffusion models (for spatial appearance, often text-to-3D or single-image-to-3D) and video diffusion models (for temporal motion). This leads to spatiotemporal inconsistency because these models were not trained jointly on 4D data and thus lack an inherent understanding of how appearance and motion should co-evolve across multiple views and time. This results in accumulated errors, appearance degradation, or unnatural motions.
   • Animate3D's innovation: Proposes MV-VDM, a foundational 4D generation model explicitly designed to jointly model spatiotemporal consistency. It's trained on a large-scale multi-view video dataset (MV-Video). This unified supervision signal ensures that motion and appearance learning do not conflict, leading to more coherent and natural dynamic 3D objects.

2. Multi-view Conditioned Animation of Existing 3D Assets:

   • Previous methods: Often rely on text- or single-view-conditioned models (e.g., Zero123) for SDS. While this can generate novel views, it struggles to faithfully preserve the multi-view attributes of an existing 3D asset, especially from angles not explicitly seen or conditioned upon. For example, a Zero123-based method might distort the back of an object if only the front is provided.
   • Animate3D's innovation: MV-VDM is conditioned on multi-view renderings of the static 3D object itself. It introduces an MV2V-Adapter that leverages these multi-view conditions, ensuring that the identity and detailed multi-view attributes of the off-the-shelf 3D asset are preserved throughout the animation process. This directly addresses the common demand to animate existing high-quality 3D content without loss of detail.

3. Data-Driven Foundational Model for 4D:

   • Previous methods: Primarily rely on distillation from existing 2D/3D/video models, which are not inherently 4D-aware.

   • Animate3D's innovation: Makes the first attempt to build a large-scale multi-view video (4D) dataset (MV-Video). This data-driven approach enables the training of MV-VDM as a true 4D foundation model, which is a paradigm shift from reliance on assembling disparate 2D/3D/video priors.

     In essence, Animate3D moves from an assemblage-of-priors approach to a unified, data-driven 4D prior approach, leading to superior results in spatiotemporal consistency and identity preservation for animating diverse 3D models.

4. Methodology

4.1. Principles

The core idea behind Animate3D is to fundamentally address the challenges of spatiotemporal inconsistency and the inability to animate multi-view 3D assets by introducing a unified 4D generation framework. This framework is built upon two main principles:

1. Foundational Multi-view Video Diffusion: Instead of separately distilling knowledge from pre-trained 3D and video diffusion models, Animate3D proposes a single, foundational Multi-view Video Diffusion Model (MV-VDM). This MV-VDM is designed to jointly learn spatial and temporal consistency by being conditioned on multi-view renderings of a static 3D object and trained on a large-scale multi-view video dataset. This ensures that the generated videos are coherent across different views and over time, and that the identity of the original 3D object is preserved.
2. Two-Stage Optimization for 4D Gaussian Splatting: To animate static 3D models efficiently and effectively, Animate3D leverages 4D Gaussian Splatting (4DGS). The animation process is an optimization task that uses the powerful priors learned by the MV-VDM. This optimization proceeds in two stages: coarse motion reconstruction from generated multi-view videos, followed by 4D Score Distillation Sampling (4D-SDS) to refine both appearance and fine-level motions. This hybrid approach ensures both overall motion accuracy and high-fidelity details.

4.2. Core Methodology In-depth (Layer by Layer)

The Animate3D framework is divided into two primary parts: learning the Multi-view Video Diffusion Model (MV-VDM) and then animating 3D objects using this MV-VDM.

The following figure (Figure 2 from the original paper) illustrates the overall Animate3D framework, showing the MV-VDM (upper part) and the 4DGS optimization pipeline (lower part).

该图像是Animate3D框架结构的示意图，展示了多视角视频扩散模型（MV-VDM）的组成及其在生成多视角视频、运动重建和细化运动4D-SDS中的流程，体现了时空注意力模块和MV2V-Adapter的设计细节。

4.2.1. Multi-view Video Diffusion Model (MV-VDM)

The MV-VDM is a novel multi-view image conditioned multi-view video diffusion model. Its design prioritizes inheriting prior knowledge from existing spatially consistent 3D models and temporally consistent video models while enhancing spatiotemporal consistency and compatibility with text prompts and object identity. The model builds upon MVDream (for 3D diffusion) and AnimateDiff (for video diffusion), leveraging their pre-trained weights. It is trained on the custom MV-Video dataset.

Spatiotemporal Attention Module

A key innovation within MV-VDM is the spatiotemporal attention module, which is inserted after the cross-attention layers in the diffusion model. This module is designed to integrate spatial and temporal information efficiently.

• Architecture: It consists of two parallel branches: one for spatial attention and another for temporal attention.

  • Spatial Branch: This branch adopts the same architecture as the multi-view 3D attention in MVDream. It converts the original 2D self-attention layer into a 3D-aware mechanism by connecting $n$ different views. Additionally, 2D spatial encoding (specifically, sinusoidal encoding) is incorporated into the latent features to further enhance spatial consistency.
  • Temporal Branch: This branch directly reuses the entire design of the temporal motion module from the video diffusion model AnimateDiff, preserving its pre-trained weights and temporal understanding.

• Feature Blending: The features from these two parallel branches are combined using an alpha blender layer with a learnable weight, denoted as $\mu$. This blending mechanism allows the model to dynamically adjust the contribution of spatial and temporal information.

• Efficiency: The parallel-branch design is chosen for efficiency and practicality, as applying full spatiotemporal attention across all views and frames simultaneously would be prohibitively memory-intensive.

  The computation for the spatiotemporal attention output ($X_{\text{out}}$) is formulated as: $$X_{\text {out }}=\mu \cdot \operatorname{Attention}_{\text {spatial}}\left(X_{l} W_{Q}^{s}, X_{l} W_{K}^{s}, X_{l} W_{V}^{s}\right) W_{O}^{s}+ \\ (1-\mu) \cdot \operatorname{Attention}_{\text {temporal }}\left(X_{r} W_{Q}^{t}, X_{r} W_{K}^{t}, X_{r} W_{V}^{t}\right) W_{O}^{t}$$ Where:

• $X_{\text{out}}$: The output feature tensor after applying spatiotemporal attention.

• $\mu$: A learnable weight parameter (scalar) that controls the blending ratio between spatial and temporal attention outputs. Its value is between 0 and 1.

• $\operatorname{Attention}_{\text{spatial}}(\cdot)$ and $\operatorname{Attention}_{\text{temporal}}(\cdot)$: Represent the attention mechanisms for the spatial and temporal branches, respectively. These functions take query, key, and value matrices as input.

• $X_l$: The input feature tensor reshaped for the spatial branch. It has dimensions $(b \times f) \times (n \times h \times w) \times c$, where batch size $b$ and frame count $f$ are grouped, and views $n$, height $h$, and width $w$ are grouped for spatial processing.

• $X_r$: The input feature tensor reshaped for the temporal branch. It has dimensions $(b \times n \times h \times w) \times f \times c$, where batch size $b$, views $n$, height $h$, and width $w$ are grouped, and frame count $f$ is processed temporally.

• $W_Q^{s}$, $W_K^{s}$, $W_V^{s}$: Projection matrices for the query, key, and value in the spatial attention branch.

• $W_O^{s}$: Output projection matrix for the spatial attention branch.

• $W_Q^{t}$, $W_K^{t}$, $W_V^{t}$: Projection matrices for the query, key, and value in the temporal attention branch.

• $W_O^{t}$: Output projection matrix for the temporal attention branch.

• $b$: Batch size, the number of multi-view video sequences processed simultaneously.

• $n$: Number of views for each frame in a multi-view video.

• $f$: Number of frames in each multi-view video.

• $h$: Height of the image features.

• $w$: Width of the image features.

• $c$: Number of channels in the image features.

Multi-view Images Conditioning (MV2V-Adapter)

To preserve the identity and fine details of the static 3D model, the MV-VDM incorporates a conditioning mechanism inspired by I2V-Adapter. A new attention layer, termed MV2V-Adapter, is added in parallel to the existing frozen multi-view 3D self-attention layer within the spatiotemporal block.

• Mechanism: Noisy frames (from $X^{1:n, i}$) are first concatenated along the spatial dimension. These concatenated features are then used as queries to extract rich contextual information from the multi-view conditional frames, which are typically the first, clean frames rendered from the static 3D object ($X^{1:n, 1}$). These conditional frames' features are extracted using the frozen 3D diffusion model. The output of the MV2V-Adapter layer is then added to the output of the original multi-view 3D attention layer of MVDream.

• Purpose: This cross-attention mechanism effectively injects the appearance information of the static object into the video generation process, allowing MV-VDM to disentangle appearance learning from motion learning.

  The formulation for the MV2V-Adapter's contribution to the output feature ($X_{\text{out}}^{1:n, i}$) for each frame $i$ is: $$X_{\text {out }}^{1: n, i}= \operatorname{Attention}\left(X^{1: n, i} W_{Q}, X^{1: n, i} W_{K}, X^{1: n, i} W_{V}\right) W_{O}+ \\ \operatorname{Attention}\left(X^{1: n, i} W_{Q}^{\prime}, X^{1: n, 1} W_{K}, X^{1: n, 1} W_{V}\right) W_{O}^{\prime}$$ Where:

• $X_{\text{out}}^{1:n, i}$: The output feature tensor for frame $i$ across all $n$ views.

• $X^{1:n, i}$: The input (noisy) feature tensor for frame $i$ across all $n$ views.

• $X^{1:n, 1}$: The input (clean) feature tensor for the first frame across all $n$ views, serving as the multi-view condition.

• $\operatorname{Attention}(\cdot)$: Represents the attention mechanism.

• $W_Q, W_K, W_V, W_O$: Projection matrices for query, key, value, and output in the original multi-view 3D attention layer of MVDream.

• $W_Q^{\prime}, W_O^{\prime}$: Projection matrices for query and output in the newly added MV2V-Adapter layer. Note that it reuses $W_K$ and $W_V$ from the original layer for keys and values from the conditional frames $X^{1:n, 1}$.

  After this, two additional cross-attention layers are employed: one inherited from MVDream to align with the text prompt, and another pre-trained in IP-Adapter to further preserve the object's identity.

Training Objectives

The training process for MV-VDM follows the principles of a Latent Diffusion Model.

• Encoding: The sampled multi-view video data $q_0^{1:n, 1:f}$ (representing $n$ views over $f$ frames) is first encoded into a latent feature $z_0^{1:n, 1:f}$ using an encoder $\mathcal{E}$ for each frame and view.

• Noise Addition: Noise is added to the latent features using a forward diffusion scheduler. Crucially, only frames from the second frame onwards ($z_0^{1:n, 2:f}$) are noised. The first frame ($z_0^{1:n, 1}$) is kept clean to serve as the multi-view image condition. The noisy latent $z_t^{1:n, 2:f}$ is obtained by: $z_{t}^{1: n, 2: f}=\sqrt{\bar{\alpha}_{t}} z_{0}^{1: n, 2: f}+\sqrt{1-\bar{\alpha}_{t}} \epsilon$ where $\bar{\alpha}_t$ is a weighted parameter and $\epsilon$ is Gaussian noise.

• Denoising: During training, the MV-VDM takes the clean latent code of the first frame $z_0^{1:n, 1}$, the noisy latent codes of subsequent frames $z_t^{1:n, 2:f}$, the text prompt embedding $y$, and the camera parameters $\Sigma^{1:n}$ as input. Its task is to predict the noise $\epsilon$ that was added.

• Loss Function: The model is supervised using an $\mathcal{L}_2$ loss between the predicted noise and the actual noise. The loss is calculated only for the latter f-1 frames.

  The training objective of MV-VDM is: $$\mathcal{L}_{\mathrm{MV}-\mathrm{VDM}}=\mathbb{E}_{\mathcal{E}\left(q_{0}\right), y, s \in \mathcal{N}(0, I), t,}\left[\left\|\epsilon-\epsilon_{\theta}\left(z_{0}^{1: n, 1}, z_{t}^{1: n, 2: f}, t, y, \Sigma^{1: n}\right)\right\|_{2}^{2}\right]$$ Where:

• $\mathcal{L}_{\mathrm{MV}-\mathrm{VDM}}$: The total loss function for training the MV-VDM.

• $\mathbb{E}$: Expectation over the distribution of encoded data $\mathcal{E}(q_0)$, text prompts $y$, sampled noise $s \in \mathcal{N}(0, I)$ (standard normal distribution), and time steps $t$.

• $q_0$: The original multi-view video data.

• $\epsilon$: The ground truth Gaussian noise added to the latent features.

• $\epsilon_{\theta}(\cdot)$: The noise predicted by the MV-VDM (parameterized by $\theta$).

• $z_0^{1:n, 1}$: The clean latent code for the first frame across all $n$ views, serving as a conditional input.

• $z_t^{1:n, 2:f}$: The noisy latent codes for frames 2 to $f$ across all $n$ views at time step $t$.

• $t$: The current diffusion time step.

• $y$: The embedding of the text prompt.

• $\Sigma^{1:n}$: Camera parameters for all $n$ views.

• $\|\cdot\|_2^2$: The squared $\mathcal{L}_2$ norm (mean squared error) between the true noise and the predicted noise.

  During training, the entire multi-view 3D attention module (from MVDream) is kept frozen. Only the MV2V-Adapter layer and the newly introduced spatiotemporal attention module are trained. This strategy conserves GPU memory and accelerates training.

4.2.2. Reconstruction and Distillation of 4DGS

Once MV-VDM is trained, it is used to animate any off-the-shelf 3D object. The chosen representation for the static 3D object is 3D Gaussian Splatting (3DGS), and it is animated by learning motion fields represented by Hex-planes, a technique commonly used in 4DGS.

4D Motion Fields

A static 3DGS is defined by a set of Gaussians $\mathcal{G}$, each characterized by:

• $\mathcal{X}$: Position (3D coordinates).

• $\mathcal{C}$: Color (e.g., RGB).

• $\alpha$: Opacity.

• $r$: Rotation (e.g., quaternion).

• $s$: Scale (e.g., 3D vector for anisotropic scaling).

  To animate this, a motion module $\mathcal{D}$ is introduced, which predicts changes in position, rotation, and scale for each Gaussian point over time. These motion fields are represented by Hex-planes. Hex-planes (or K-planes) are an explicit representation for dynamic scenes, where features are stored on six 2D planes ((x,y), (x,z), (y,z), (x,t), (y,t), (z,t)). By interpolating features from these planes at a given spatial location and time, motion features can be derived.

The computation of motion fields involves interpolating features from Hex-planes and then mapping these features to changes in Gaussian properties: $$\mathcal{F}=\bigcup_{i} \prod_{\zeta} \operatorname{interp}\left(R^{\zeta},(\mathcal{X}, i)\right)$$ $$\Delta \mathcal{X}=\phi_{\mathcal{X}}(\mathcal{F}), \Delta r=\phi_{r}(\mathcal{F}), \Delta s=\phi_{s}(\mathcal{F})$$ Where:

• $\mathcal{F}$: The aggregated motion features for a Gaussian at a specific time.

• $\bigcup_{i}$: This notation is slightly unusual in this context for a single point, but typically implies some form of aggregation or summation over contributing factors/planes.

• $\prod_{\zeta}$: This implies a product (or combination) over features interpolated from different Hex-planes.

• $\zeta$: Denotes one of the six Hex-planes (e.g., (x, y), (x, z), (y, z), (x, t), (y, t), (z, t)). The planes involving $t$ (time) are crucial for dynamics.

• $\operatorname{interp}(\cdot)$: An interpolation function that retrieves features from the Hex-plane $R^{\zeta}$ at a given spatial coordinate $\mathcal{X}$ and time $i$.

• $R^{\zeta}$: The Hex-plane for a specific dimension combination $\zeta$, which stores learned motion features.

• $(\mathcal{X}, i)$: The spatial position $\mathcal{X}$ and time step $i$ at which motion features are queried.

• $\Delta \mathcal{X}$: The predicted change in position for a Gaussian.

• $\Delta r$: The predicted change in rotation for a Gaussian.

• $\Delta s$: The predicted change in scale for a Gaussian.

• $\phi_{\mathcal{X}}(\cdot)$, $\phi_r(\cdot)$, $\phi_s(\cdot)$: Learnable functions (e.g., small MLPs) that map the interpolated motion features $\mathcal{F}$ to the respective changes in position, rotation, and scale.

  With these predicted changes, the Gaussian $\mathcal{G}$ at time $t$ is updated to $\mathcal{G}'$: $$\mathcal{G}^{\prime}=\{\mathcal{X}+\Delta \mathcal{X}, \mathcal{C}, \alpha, r+\Delta r, s+\Delta s\}$$ Where:

• $\mathcal{G}'$: The updated Gaussian representation at time $t$.

• $\mathcal{X}+\Delta \mathcal{X}$: The new position of the Gaussian.

• $\mathcal{C}$: The color, which remains unchanged to preserve the static object's appearance.

• $\alpha$: The opacity, which also remains unchanged.

• $r+\Delta r$: The new rotation of the Gaussian.

• $s+\Delta s$: The new scale of the Gaussian.

Coarse Motion Reconstruction

The first stage of optimization involves coarse motion reconstruction. This leverages the spatiotemporal consistent multi-view videos generated by the trained MV-VDM as targets. A simple $\mathcal{L}_2$ loss is used to make the rendered 4DGS output match the target videos.

The reconstruction loss $\mathcal{L}_{\text{rec}}$ is calculated as: $$\mathcal{L}_{\mathrm{rec}}=\sum_{i=1}^{n} \sum_{j=1}^{f}\|\mathcal{C}-\widehat{\mathcal{C}}\|^{2}$$ Where:

• $\mathcal{L}_{\mathrm{rec}}$: The reconstruction loss.

• $\sum_{i=1}^{n}$: Summation over all $n$ views.

• $\sum_{j=1}^{f}$: Summation over all $f$ frames.

• $\mathcal{C}$: The multi-view and multi-frame renderings produced by the current 4DGS model.

• $\widehat{\mathcal{C}}$: The corresponding ground truth multi-view and multi-frame videos generated by MV-VDM.

• $\|\cdot\|^2$: The squared $\mathcal{L}_2$ norm, calculating the pixel-wise mean squared error.

  This stage effectively learns high-quality coarse motions, as MV-VDM provides strong, consistent supervision.

4D-SDS Optimization

To further model fine-level motions and enhance details, a 4D-SDS optimization stage is introduced. This distills knowledge from the MV-VDM itself. It is a variant of the $\mathbf{z}_0$-reconstruction SDS loss.

The 4D-SDS loss $\mathcal{L}_{\mathrm{4D}-\mathrm{SDS}}$ is formulated as: $$\mathcal{L}_{\mathrm{4D}-\mathrm{SDS}}(\mathcal{G}, \mathcal{D}, z=\mathcal{E}(g(\mathcal{D}(\mathcal{G}))))=\mathbb{E}_{t, \Sigma, \epsilon}\left[\left\|z-\hat{z_{0}}\right\|_{2}^{2}\right], \quad \hat{z_{0}}=\frac{z_{t}-\sigma_{t} \epsilon_{\theta}}{\alpha_{t}}$$ Where:

• $\mathcal{L}_{\mathrm{4D}-\mathrm{SDS}}$: The 4D Score Distillation Sampling loss.
• $\mathcal{G}$: The current 3DGS representation (static part).
• $\mathcal{D}$: The motion module (Hex-planes) that predicts dynamics.
• $z$: The latent feature of a rendered image. This is obtained by encoding a rendered image $g(\mathcal{D}(\mathcal{G}))$ (where $g$ is the rendering function and $\mathcal{D}(\mathcal{G})$ is the animated Gaussian representation) using the encoder $\mathcal{E}$.
• $\mathbb{E}_{t, \Sigma, \epsilon}$: Expectation over sampled time steps $t$, camera parameters $\Sigma$, and Gaussian noise $\epsilon$.
• $\|z-\hat{z_0}\|_2^2$: The squared $\mathcal{L}_2$ norm between the latent feature $z$ and the estimated clean latent feature $\hat{z_0}$.
• $\hat{z_0}$: The estimation of the clean latent feature, derived from the current noisy latent $z_t$ and the noise predicted by the diffusion model $\epsilon_{\theta}$. This is a common way to estimate the original clean input in diffusion models.
• $z_t$: The noisy latent feature at time step $t$.
• $\sigma_t$: The noise scale, controlled by the noise scheduler.
• $\epsilon_{\theta}$: The noise predicted by the MV-VDM (parameterized by $\theta$) at time $t$ for $z_t$.
• $\alpha_t$: The signal scale (related to $\bar{\alpha}_t$), controlled by the noise scheduler.

As-Rigid-As-Possible (ARAP) Loss

To facilitate rigid movement learning and maintain the high-quality appearance of the static object, a variant of the As-Rigid-As-Possible (ARAP) loss is introduced. This loss encourages local rigidity in the deformation of the Gaussian points.

The ARAP loss $\mathcal{L}_{\text{arap}}$ is defined as: $$\mathcal{L}_{\text {arap }}\left(p_{j}\right)=\sum_{i=2}^{f} \sum_{k \in \mathcal{N}_{c_{i}}} w_{j, k}\left\|\left(p_{j}^{i}-p_{k}^{i}\right)-R_{j}\left(\left(p_{j}^{1}-p_{k}^{1}\right)\right)\right\|^{2}$$ Where:

• $\mathcal{L}_{\text{arap}}(p_j)$: The ARAP loss calculated for a point $p_j$.

• $\sum_{i=2}^{f}$: Summation over frames from 2 to $f$. The first frame is typically static or used as reference.

• $\sum_{k \in \mathcal{N}_{c_{i}}}$: Summation over neighboring points $p_k$ within a fixed radius of $p_j$ in frame $i$. $\mathcal{N}_{c_i}$ represents this set of neighbors.

• $w_{j,k}$: A weight factor, $w_{j,k}=\exp\left(-\frac{d_{jk}}{d}\right)$, where $d_{jk}$ is the distance between the centers of $p_j$ and $p_k$, and $d$ is a scaling parameter. This weight indicates the influence of $p_k$ on $p_j$.

• $p_j^i$: The position of point $j$ at frame $i$.

• $p_k^i$: The position of a neighbor point $k$ at frame $i$.

• $p_j^1$: The initial position of point $j$ at the first frame.

• $p_k^1$: The initial position of a neighbor point $k$ at the first frame.

• $R_j$: An estimated rigid transformation (rotation matrix) for point $j$. It represents the local rigid motion of the neighborhood around $p_j$.

  The rigid transformation $\hat{R}_j$ is estimated for each point $p_j$ using Singular Value Decomposition (SVD): $$\hat{R}_{j}=\operatorname{argmin}_{R \in \mathbf{S O}(3)} \sum_{k \in \mathcal{N}_{c_{i}}} w_{j, k}\left\|\left(p_{j}^{i}-p_{k}^{i}\right)-\hat{R}_{j}\left(\left(p_{j}^{1}-p_{k}^{1}\right)\right)\right\|^{2}$$ Where:

• $\hat{R}_j$: The estimated optimal rotation matrix in the Special Orthogonal Group SO(3) (i.e., a rotation matrix).

• $\operatorname{argmin}_{R \in \mathbf{S O}(3)}$: Denotes finding the rotation matrix $R$ that minimizes the following sum. This is a standard formulation for finding the best rigid alignment.

Total Training Objectives for 4DGS

The overall training objective for animating the off-the-shelf 3DGS object is a weighted sum of the reconstruction loss, 4D-SDS loss, and ARAP loss: $$\mathcal{L}=\lambda_{1} \mathcal{L}_{\mathrm{rec}}+\lambda_{2} \mathcal{L}_{\mathrm{4D}-\mathrm{SDS}}+\lambda_{3} \mathcal{L}_{\text {arap }}$$ Where:

• $\mathcal{L}$: The total loss function to be minimized during the 4DGS optimization.
• $\lambda_1, \lambda_2, \lambda_3$: Weighted parameters that control the importance of each loss term.

4.2.3. Extension to Mesh Animation

The framework is also extended to animate high-quality meshes, which are typically generated by commercial 3D tools or human artists.

• Initialization: The 3DGS representation for the mesh is initialized directly from the mesh's vertices and triangles. Vertex colors determine the Gaussian colors, and connected edges are averaged for scale. Opacity is set to fully visible, and rotation to zero.
• Animation: The coarse 3DGS is animated using the motion reconstruction steps described above. The per-vertex Gaussian trajectory (i.e., how each Gaussian moves over time) is then directly used to deform the static mesh.
• Simplicity: A key advantage is that this process does not require traditional skeleton rigging, control point selection, or complex deformation algorithms, making it straightforward to apply.

5. Experimental Setup

5.1. Datasets

5.1.1. Training Dataset (MV-Video)

To train the MV-VDM, the authors constructed a novel, large-scale multi-view video dataset named MV-Video.

• Source: The dataset is built from 37,857 animated 3D models collected from Sketchfab (a popular online platform for 3D models). Models that did not allow use for AI programs were filtered out.
• Scale: These models provided an average of 2.2 animations per ID, resulting in a total of 83,716 animations. Each animation is 2 seconds long at 24 frames per second (fps). From these, over 1.3 million multi-view videos were rendered.
• Characteristics: minigpt4-video was used to generate text prompts for these animations, which serve as conditionings for the MV-VDM.
• Rendering Details (Appendix D.1):

  • Models were centralized based on their bounding box in the first frame.

  • Camera distance was adjusted to keep the object within view during animation.

  • Sixteen views were sampled evenly in terms of azimuth (randomly selected between $-11.25^{\circ}$ and $11.25^{\circ}$) and elevation ($0^{\circ}$ to $30^{\circ}$).

  • Challenging models (large movements, complex interactions, high speed, sudden appearance changes) were manually filtered to stabilize training.

    The following table (Table 1 from the original paper) presents the statistical information for the MV-Video dataset:

    Model ID	Animations	Avg. Animations per ID	Max Animations per ID	Multi-view Videos
    37,857	83,716	2.2	6.0	1,339,456

The following figure (Figure 13 from the original paper) shows a word cloud of the top 1000 nouns extracted from the text captions of the MV-Video dataset, indicating a diverse range of animated 3D object categories.

该图像是词云图，展示了与Animate3D及相关动画、3D模型、动作和场景等关键词的频率和关联度，突出“character”、“woman”、“man”等词汇，反映了研究内容的核心主题。

5.1.2. Evaluation Dataset

• For MV-VDM evaluation: 128 static 3D objects were used. Multi-view videos were generated conditioned on these objects. Four different random seeds were used for each object, and average results were reported.

• For 4D generation evaluation: 25 objects across various categories were generated using the large 3DGS reconstruction model GRM.

• Input Images and Prompts: The appendix provides examples of input images for image-to-3D generation and corresponding text prompts used for 4D animation (as shown in Figure 10).

  The following figure (Figure 10 from the original paper) illustrates the input images for image-to-3D generation and corresponding prompts for 4D animation used in the evaluation:

  该图像是一张插图，展示了多种卡通风格的3D动物和物体，每个形象配有简短的英文描述，表现其动作和特征，如跳跃、行走、挥动等动态效果。

5.2. Evaluation Metrics

The evaluation protocol follows VBench, a comprehensive benchmark suite for video generative models, specifically its I2V (Image-to-Video) evaluation protocol. Out of the 9 available I2V metrics, four were selected for evaluation, and the rationale for omitting others is provided.

The chosen metrics are:

• I2V Subject (I2V) $\uparrow$:

  1. Conceptual Definition: This metric assesses how well the appearance of the main object in the generated video remains consistent with its appearance in the initial input image. It focuses on preserving the identity and visual details of the subject across time.
  2. Mathematical Formula: The paper does not provide an explicit formula but states it uses DINO feature similarity across frames. Assuming a common approach for I2V Subject evaluation with DINO features: $ \text{I2V Subject} = \frac{1}{F} \sum_{f=1}^{F} \text{cosine_similarity}(\text{DINO}(I_{\text{input}}), \text{DINO}(I_{f})) $
  3. Symbol Explanation:
     • $F$: Total number of frames in the generated video.
     • $f$: Index of the current frame, from 1 to $F$.
     • $I_{\text{input}}$: The initial input image (static 3D object rendering) used as a reference.
     • $I_f$: The $f$-th frame of the generated video.
     • $\text{DINO}(\cdot)$: A function that extracts high-level semantic features from an image using a self-supervised vision transformer model like DINO (e.g., Vision Transformer features).
     • $\text{cosine\_similarity}(A, B)$: A measure of the cosine of the angle between two non-zero vectors $A$ and $B$, indicating their similarity. It is defined as $\frac{A \cdot B}{\|A\|_2 \|B\|_2}$. A higher value (closer to 1) indicates greater similarity.

• Motion Smoothness (M. Sm.) $\uparrow$:

  1. Conceptual Definition: This metric evaluates the fluidity and naturalness of the motion in the generated video. It aims to quantify whether the movements appear continuous and adhere to physical laws, rather than being jerky or unnatural.
  2. Mathematical Formula: The paper states that motion prior in the video frame interpolation model is utilized for evaluation, but does not provide an explicit formula. Typically, motion smoothness can be evaluated by analyzing optical flow vectors or frame interpolation errors. A simplified representation could involve the variance of motion vectors. $ \text{M. Sm.} \propto \frac{1}{F-1} \sum_{f=1}^{F-1} \exp\left(-\beta \cdot |\text{Motion}_{f+1} - \text{Motion}_f|^2\right) $
  3. Symbol Explanation:
     • $F$: Total number of frames.
     • $\text{Motion}_f$: A representation of motion between frame $f$ and $f+1$ (e.g., optical flow vectors).
     • $\|\cdot\|^2$: Squared Euclidean norm.
     • $\beta$: A scaling factor.
     • A higher value of M. Sm. implies smoother motion. The actual VBench implementation uses a more sophisticated metric derived from a video frame interpolation model which is trained to predict plausible intermediate frames.

• Dynamic Degree (Dy. Deg.) (No $\uparrow$ or $\downarrow$ indicated for target performance):

  1. Conceptual Definition: This metric quantifies the amount of motion or activity present in the synthesized video. It indicates how dynamic the generated content is. The paper notes that sometimes completely failed results can present extremely high dynamic degrees, so a higher value is not always better.
  2. Mathematical Formula: The paper mentions RAFT (Recurrent All-Pairs Field Transforms) is employed to estimate the degree of dynamics. RAFT computes dense optical flow. The Dynamic Degree is likely derived from the magnitude of these optical flow vectors averaged over frames. $ \text{Dy. Deg.} = \frac{1}{F-1} \sum_{f=1}^{F-1} \text{MeanMagnitude}(\text{RAFT}(I_f, I_{f+1})) $
  3. Symbol Explanation:
     • $F$: Total number of frames.
     • $I_f, I_{f+1}$: Consecutive frames in the video.
     • $\text{RAFT}(I_f, I_{f+1})$: The dense optical flow field computed between frame $f$ and frame $f+1$ by the RAFT model.
     • $\text{MeanMagnitude}(\cdot)$: The average magnitude of the motion vectors in the optical flow field. A higher magnitude indicates more motion.

• Aesthetic Quality (Aest. Q.) $\uparrow$:

  1. Conceptual Definition: This metric evaluates the perceived artistic and beauty value of each frame in the generated video, reflecting human aesthetic judgment.
  2. Mathematical Formula: The paper states that it is calculated by the LAION aesthetic predictor. This predictor is a pre-trained neural network (e.g., a CLIP-based model fine-tuned on aesthetic ratings) that outputs a score indicating the aesthetic appeal of an image. $ \text{Aest. Q.} = \frac{1}{F} \sum_{f=1}^{F} \text{LAION_Predictor}(I_f) $
  3. Symbol Explanation:

     • $F$: Total number of frames.

     • $I_f$: The $f$-th frame of the generated video.

     • $\text{LAION\_Predictor}(\cdot)$: A pre-trained model (e.g., from LAION) that takes an image as input and outputs a scalar score representing its aesthetic quality. A higher score indicates better aesthetic quality.

       The paper notes that for all metrics, higher values are generally better, except for Dynamic Degree where extremely high values can sometimes indicate failed or unstable results.

5.3. Baselines

The paper compares Animate3D against two state-of-the-art 4D generation methods:

• 4Dfy [5]: This method is a text-to-4D generation framework that uses hybrid score distillation sampling. It initially generates a static 3D object using 3D-SDS and then animates it via video SDS and single-view video reconstruction. While 4Dfy originally adopted dynamic NeRF [34, 26] as its 4D representation, for a fair comparison, the authors replaced it with 4DGS [55] (the same representation used in Animate3D and DG4D) and also applied the ARAP loss for motion regularization.

• DreamGaussian4D (DG4D) [38]: This method also represents a state-of-the-art approach for generative 4D Gaussian Splatting. Similar to 4Dfy, it starts by generating a static 3D object and then animates it using video SDS and single-view video reconstruction. DG4D already uses 4DGS as its representation and incorporates motion regularizations, which were kept unchanged for the comparison.

  These baselines are representative because they employ the common distillation paradigm from pre-trained 2D/3D and video diffusion models, which Animate3D aims to improve upon with its unified MV-VDM approach.

6. Results & Analysis

6.1. Core Results Analysis

The experimental results demonstrate Animate3D's significant advantages over existing state-of-the-art methods in 4D generation.

6.1.1. Quantitative Comparison

The following table (Table 2a from the original paper) presents a quantitative comparison of Animate3D with 4Dfy and DG4D using video generation metrics.

| (a) Comparison on video generation metrics. | | | | | :--: | :--: | :--: | :--: | :--: | | I2V $\uparrow$ | M. Sm. $\uparrow$ | Dy. Deg. | Aest. Q. $\uparrow$ | 4Dfy (Gau.) [5] | 0.783 | 0.996 | 0.0 | 0.497 | DG4D [38] | 0.898 | 0.986 | 0.477 | 0.529 | Ours | 0.982 | 0.991 | 0.597 | 0.581

Analysis of Table 2a:

• I2V Subject (I2V $\uparrow$): Animate3D achieves the highest score (0.982), significantly outperforming DG4D (0.898) and 4Dfy (0.783). This indicates Animate3D's superior ability to maintain the identity and appearance consistency of the given static 3D object in the generated video.

• Motion Smoothness (M. Sm. $\uparrow$): Animate3D (0.991) performs very well, only slightly behind 4Dfy (0.996). The paper attributes 4Dfy's slightly higher score to its tendency to generate nearly static results (as indicated by its Dynamic Degree of 0.0), where a lack of motion can trivially lead to high smoothness. Animate3D achieves high smoothness while also generating substantial motion.

• Dynamic Degree (Dy. Deg.): Animate3D has the highest Dynamic Degree (0.597) among the actively animating methods, showing it generates more substantial and expressive motions compared to DG4D (0.477) and especially 4Dfy (0.0).

• Aesthetic Quality (Aest. Q. $\uparrow$): Animate3D also leads in Aesthetic Quality (0.581), indicating that its generated videos are visually more appealing compared to DG4D (0.529) and 4Dfy (0.497).

  The quantitative results strongly suggest that Animate3D is capable of animating 3D objects with smooth and dynamic motion while preserving their high-quality appearance, a crucial balance that previous methods struggled to achieve.

6.1.2. Qualitative Comparison

The following figure (Figure 3 from the original paper) provides a qualitative comparison of Animate3D with state-of-the-art methods on animating various 3D models (a bear, a frog, and a penguin) at different time points.

Analysis of Figure 3:

• 4Dfy: The results from 4Dfy are notably blurred and show significant deviation from the original 3D objects. This is linked to its reliance on text-conditioned diffusion models for optimization, which often leads to appearance degradation. Furthermore, 4Dfy's outputs are almost static, confirming the 0.0 Dynamic Degree seen in the quantitative results. The paper explains this by noting that during early training, the initially static noisy rendered image sequence can mislead video diffusion models into generating static supervisions.

• DreamGaussian4D (DG4D): DG4D shows better alignment with the given 3D object when viewed from the front (the view typically used for guiding video generation). However, it struggles with novel views, showing distortions in areas like the bear's goggles, the penguin's back and side views, and the frog's tail. This limitation arises from its use of Zero123 for novel view optimization, which conditions on a single front view and can lead to appearance degradation in unconditioned views. A critical failure mode for DG4D is its misinterpretation of motion towards the camera. For example, a frog moving back and forth is misinterpreted as the object enlarging and reducing, leading to blurry effects and strange appearances. The penguin's nodding motion towards the camera also results in similar issues.

• Ours (Animate3D): In contrast, Animate3D successfully handles motion towards the camera, as demonstrated by the bear's raised front paw. It effectively maintains the high-quality appearance of the input 3D object while generating natural and coherent motion, benefiting from its spatiotemporal consistent multi-view prior.

  The qualitative results strongly reinforce the quantitative findings, showcasing Animate3D's ability to produce visually superior and more consistent animations.

The following figure (Figure 7 from the original paper) provides more qualitative comparisons on reconstructed 3D objects.

Analysis of Figure 7: Similar patterns are observed here. 4Dfy generates blurry and inconsistent results, often static. DreamGaussian4D shows distortions and blurriness, especially in novel views. Animate3D consistently produces spatially and temporally coherent animations that are faithful to the input 3D object. This highlights the generalizability of Animate3D across different types of 3D objects (generated vs. reconstructed).

6.1.3. User Study

To further validate the perceived quality, a user study was conducted with 20 participants evaluating 25 dynamic objects. Participants scored the generated animations from 1 to 5 based on alignment with text, alignment with static 3D object, motion quality, and appearance quality.

The following table (Table 2b from the original paper) presents the averaged results from the user study.

| (b) Comparison via user study. | | | | | :--: | :--: | :--: | :--: | :--: | | Align. Text | Align. 3D. | Mot. Appr. | Total | 4Dfy(Gau.) [5] | 2.028 | 1.608 | 1.534 | 1.84 | DG4D [38] | 2.824 | 3.52 | 2.284 | 3.108 | Ours | 4.386 | 4.734 | 4.288 | 4.528

Analysis of Table 2b: Animate3D received significantly higher scores across all categories, with a total average of 4.528, far surpassing DG4D (3.108) and 4Dfy (1.84). This user study confirms the subjective superiority of Animate3D in generating animations that are well-aligned with both textual descriptions and the original 3D object, while also possessing high-quality motion and appearance.

6.1.4. Extended Comparison with Other 4D Generation Methods

The paper includes a broader comparison with other open-sourced 4D generation works in Appendix B.

The following table (Table 4 from the original paper) summarizes this comparison.

Analysis of Table 4: This extended comparison confirms Animate3D's leading performance.

• Animate3D (both 8-frame and 16-frame versions) consistently achieves the highest or second-highest scores across I2V Subject, Aesthetic Quality, and CLIP-I (a measure of image-text alignment, not fully defined in the main paper but commonly understood as CLIP Image-Text Similarity).

• For Dynamic Degree, TC4D (0.830) achieves the highest score, but it takes pre-defined object trajectory as input, which is a different task setup. Animate3D (16-frame) comes in second at 0.750, demonstrating strong dynamic capabilities in a more general setup.

• For Motion Smoothness, 4Dfy and Dream-in-4D show slightly higher values, but these are correlated with their lower Dynamic Degree (often generating nearly static results), as discussed previously. Animate3D provides a good balance.

  This comprehensive quantitative evaluation across multiple metrics and baselines strongly supports the paper's claim of superior performance.

6.2. Ablation Studies / Parameter Analysis

Ablation studies were conducted to validate the effectiveness of the proposed components of MV-VDM and the 4DGS optimization pipeline.

6.2.1. Multi-view Video Diffusion Ablation

The following table (Table 3a from the original paper) shows the ablation results for Multi-view Video Diffusion.

| (a) Ablation of Multi-View Diffusion | | | | | :--: | :--: | :--: | :--: | :--: | | 12V $\uparrow$ | M. Sm. $\uparrow$ | Dy. Deg. | Aest. Q. $\uparrow$ | w/o S.T. Att. | 0.915 | 0.980 | 0.958 | 0.531 | w/o Pre-train | 0.910 | 0.981 | 0.944 | 0.531 | Ours | 0.935 | 0.988 | 0.710 | 0.532

Analysis of Table 3a:

• w/o S.T. Att. (without Spatiotemporal Attention): Removing the spatiotemporal attention module leads to a drop in I2V Subject (0.915 vs. 0.935), Motion Smoothness (0.980 vs. 0.988), and Aesthetic Quality (0.531 vs. 0.532). Although Dynamic Degree appears higher (0.958 vs. 0.710), the paper clarifies this is due to an increase in unstable failure cases rather than truly improved dynamics.

• w/o Pre-train (without pre-trained weights from video diffusion model): Similar to w/o S.T. Att., this also results in performance degradation across I2V Subject (0.910), Motion Smoothness (0.981), and Aesthetic Quality (0.531), with an artificially high Dynamic Degree (0.944) indicating instability.

  The qualitative ablations in the following figure (Figure 4 from the original paper) further illustrate these points:

  

  Analysis of Figure 4: The visual results for w/o S.T. Att. and w/o Pre-train show significant artifacts, blurred details, and less coherent motion compared to the "Ours" model (full model). The basketball player's movements are less crisp, and the overall visual quality is degraded. This confirms that both the proposed spatiotemporal attention module and the use of pre-trained weights from the video diffusion model are critical for generating multi-view videos that are consistent with input images and exhibit high-quality appearance and motion.

6.2.2. 4D Object Optimization Ablation

The following table (Table 3b from the original paper) shows the ablation results for 4D Object Optimization.

| (b) Ablation of 4D Generation | | | | | :--: | :--: | :--: | :--: | :--: | | 12V $\uparrow$ | M. Sm. $\uparrow$ | Dy. Deg. | Aest. Q. $\uparrow$ | w/o SDS loss | 0.978 | 0.990 | 0.657 | 0.572 | w/o ARAP loss | 0.970 | 0.990 | 0.573 | 0.557 | Ours | 0.983 | 0.997 | 0.597 | 0.581

Analysis of Table 3b:

• w/o SDS loss: Removing the 4D-SDS loss leads to a slight decrease in I2V Subject (0.978 vs. 0.983), Motion Smoothness (0.990 vs. 0.997), and Aesthetic Quality (0.572 vs. 0.581).

• w/o ARAP loss: Removing the As-Rigid-As-Possible (ARAP) loss results in a more noticeable drop across I2V Subject (0.970), Motion Smoothness (0.990), and Aesthetic Quality (0.557).

• Dynamic Degree: For both ablation cases, the Dynamic Degree is higher (0.657 and 0.573) compared to the full model (0.597). The authors explain that this apparent increase is mainly due to the removal of floaters and blurry effects when SDS and ARAP losses are applied, which can contribute to the Dynamic Degree metric in a misleading way.

  The qualitative ablations in the following figure (Figure 5 from the original paper) support these findings:

  

  Analysis of Figure 5: The "w/o SDS loss" and "w/o ARAP loss" results show more artifacts and less refined motion compared to the full model. For example, w/o ARAP loss introduces significant distortion, especially around the leg, indicating a failure to maintain local rigidity. The conclusion is that both 4D-SDS and ARAP losses significantly improve the alignment with the 3D object, motion smoothness, and aesthetic quality, even if they might slightly reduce the absolute motion amplitude by eliminating undesirable artifacts. The overall performance is improved with their inclusion.

The following figure (Figure 8 from the original paper) provides more qualitative ablations, further demonstrating the positive impact of each component.

Analysis of Figure 8: The additional ablation examples for a dinosaur, a treasure chest, and a panda further reinforce the conclusions. Removing S.T. Att., Pre-train, or ARAP loss consistently leads to degraded visual quality, less coherent motions, and artifacts (e.g., blurry details, fragmented parts, unnatural deformations). This highlights the synergistic effect of the proposed modules and losses in achieving high-quality 4D generation.

6.2.3. Mesh Animation

The paper demonstrates the applicability of Animate3D to mesh animation, where static meshes from commercial tools are animated.

The following figure (Figure 6 from the original paper) showcases examples of mesh animation.

Analysis of Figure 6: The visualizations of a wooden dragon head and a cute dog demonstrate successful mesh animation. The wooden dragon head is shown shaking from right to left, and the dog is captured running and jumping. These results are generated simply by deforming the static mesh based on the per-vertex Gaussian trajectory learned by the framework, without complex skeleton rigging or deformation algorithms. This indicates the practicality and effectiveness of the learned motion fields for direct application to existing mesh assets.

6.3. Computational Resources

The paper provides specific details on the computational resources used:

• MV-VDM Training: It took 3 days to train the MV-VDM on 32 80G A800 GPUs.

• 4D Generation Optimization: The optimization process for 4D generation (animating a single object) takes approximately 30 minutes on a single A800 GPU.

  These details provide a clear understanding of the computational cost involved in developing and utilizing Animate3D.

7. Conclusion & Reflections

7.1. Conclusion Summary

In summary, Animate3D presents a pioneering framework for animating any off-the-shelf 3D object. It ingeniously decouples the complex task of 4D object generation into two main stages:

1. Foundational 4D Generation Model: The introduction of MV-VDM, the first 4D foundation model, which is capable of generating spatiotemporally consistent multi-view videos. This model is uniquely conditioned on multi-view renderings of a static 3D object, ensuring identity preservation.

2. Joint 4DGS Optimization Pipeline: An effective, two-stage pipeline built upon MV-VDM to animate 3D objects represented by 4D Gaussian Splatting (4DGS). This pipeline combines reconstruction from generated MV-VDM videos and 4D Score Distillation Sampling (4D-SDS) for fine-grained motion and appearance refinement, further augmented by an As-Rigid-As-Possible (ARAP) loss for realistic movement.

   Crucially, the development of MV-VDM was made possible by the creation of MV-Video, the largest multi-view video (4D) dataset to date, containing over 1.3 million multi-view videos from approximately 84,000 animations. The extensive qualitative and quantitative experiments, including user studies and ablations, robustly demonstrate that Animate3D significantly surpasses previous approaches in generating spatiotemporally consistent 4D objects with high fidelity. The framework's ability to animate existing 3D assets, including direct mesh animation without complex rigging, makes it a highly practical solution for various downstream 4D applications. The commitment to open-sourcing the data, code, and pre-trained weights promises to further accelerate research in this field.

7.2. Limitations & Future Work

The authors candidly discuss several limitations of Animate3D, offering clear directions for future research:

1. Animation Time: Animating an existing 3D object still requires a relatively long time, approximately 30 minutes. Future work could focus on optimizing the 4DGS training process for faster inference.
2. Temporal Coherence vs. Motion Amplitude Trade-off: There is an observed trade-off in the MV-VDM generated multi-view videos: larger motion amplitudes tend to increase the risk of temporal incoherence. Addressing this balance to allow for more expressive yet perfectly coherent motions is an important area for improvement.
3. Domain Gap: The model sometimes struggles to animate realistic scenes effectively. This is primarily attributed to the domain gap between the synthetic training data (MV-Video) and real-world test data. Bridging this gap, possibly through incorporating more diverse real-world data or domain adaptation techniques, is a potential future direction.
4. Evaluation Metrics for 4D Generation: The current evaluation metrics in 4D generation are noted as insufficient, largely relying on video generation metrics and user studies. The authors suggest that designing more suitable and comprehensive metrics specifically for 4D generation will be an important future work.

7.3. Personal Insights & Critique

This paper represents a significant step forward in 4D content creation. The core innovation of introducing a unified multi-view video diffusion model (MV-VDM) trained on a massive, dedicated multi-view video dataset (MV-Video) fundamentally addresses the spatiotemporal inconsistency issues that plague previous distillation-based methods. By integrating spatial and temporal attention from the ground up, Animate3D offers a more holistic approach to 4D modeling.

The ability to animate any off-the-shelf 3D model while preserving its identity and multi-view attributes is a huge win for practical applications in AR/VR, gaming, and film production. The mesh animation extension, requiring no complex rigging, further enhances its utility, simplifying pipelines for artists and developers. The transparent discussion of limitations is also commendable, particularly highlighting the domain gap and the need for better 4D-specific evaluation metrics. The computational cost, while not prohibitive for single-object animation, could be a bottleneck for large-scale production, suggesting that efficiency improvements would be highly valuable.

From a critical perspective, while the MV-Video dataset is a monumental effort, its synthetic nature inherently limits its ability to fully capture the complexity and nuances of real-world physics and interactions, as acknowledged by the domain gap limitation. Future work could explore incorporating real-world motion capture data or techniques that improve the realism of synthetic motions. Additionally, the trade-off between temporal coherence and motion amplitude hints at an ongoing challenge in generative models – balancing creativity with fidelity. Research into adaptive regularization or motion priors could potentially alleviate this. The call for better 4D evaluation metrics is also crucial; the field needs robust, objective measures that capture the intricate aspects of dynamic 3D content beyond simple frame-wise similarities.

Overall, Animate3D provides a robust, effective, and highly practical solution for 4D generation, marking a clear progression towards more unified and data-driven approaches in the field. Its open-source release will undoubtedly foster further innovation.