1. Bibliographic Information

1.1. Title

Mitty: Diffusion-based Human-to-Robot Video Generation

1.2. Authors

Yiren Song, Cheng Liu, Weijia Mao, Mike Zheng Shou†. Affiliation: Show Lab, National University of Singapore.

1.3. Journal/Conference

The paper is listed as a preprint on arXiv. While the specific conference or journal for peer review is not explicitly stated in the provided abstract, publishing on arXiv is a common practice for disseminating research findings rapidly within the machine learning and robotics communities. Given the quality and scope, it is likely intended for a top-tier computer vision, machine learning, or robotics conference.

1.4. Publication Year

2025 (specifically, published on arXiv at 2025-12-19T05:52:15.000Z).

1.5. Abstract

The paper introduces Mitty, a novel Diffusion Transformer framework for end-to-end Human2Robot (H2R) video generation. This approach aims to allow robots to learn directly from human demonstration videos, overcoming limitations of existing methods that rely on intermediate representations (like keypoints or trajectories), which often lead to information loss and cumulative errors. Mitty builds upon a pretrained video diffusion model (Wan 2.2), leveraging its strong visual-temporal priors to directly translate human demonstrations into robot-execution videos without needing action labels or intermediate abstractions. A key technical innovation is compressing demonstration videos into condition tokens that are fused with robot denoising tokens via bidirectional attention during the diffusion process. To address paired-data scarcity, the authors developed an automatic synthesis pipeline that generates high-quality human-robot video pairs from large egocentric datasets like EPIC-Kitchens. Experiments on the Human2Robot dataset and EPIC-Kitchens demonstrate that Mitty achieves state-of-the-art results, exhibits strong generalization to unseen environments, and offers new insights for scalable robot learning from human observations.

1.6. Original Source Link

Official Source: https://arxiv.org/abs/2512.17253 PDF Link: https://arxiv.org/pdf/2512.17253v1.pdf Publication Status: Preprint on arXiv.

2. Executive Summary

2.1. Background & Motivation

The core problem the paper aims to solve is enabling robots to learn new manipulation skills directly from human demonstration videos and generate corresponding robot-execution videos. This Human2Robot (H2R) video generation is a crucial step towards scalable and generalizable robot learning, allowing robots to acquire skills rapidly by observing humans, similar to how humans learn.

This problem is highly important in the current field because traditional robot learning methods, such as teleoperation or reinforcement learning, are often costly, time-consuming, and struggle with generalization across diverse tasks and environments. Existing approaches to H2R learning typically rely on intermediate representations like keypoints, trajectories, or depth maps. While intuitive, these methods introduce several challenges:

• Information Loss: Intermediate representations may fail to capture the fine-grained spatio-temporal dynamics essential for robust generalization.

• Cumulative Errors: Errors in the intermediate estimation stage (e.g., inaccurate keypoint detection) can accumulate and severely degrade the quality and consistency of the generated robot actions.

• Appearance and Scene Consistency: Ensuring the generated robot video matches the human demonstration's scene while maintaining a plausible robot embodiment is challenging.

• Action and Strategy Alignment: Adapting human actions to a robot's different kinematics and physical form is complex.

• Data Scarcity: High-quality, finely aligned human-robot video pairs are extremely rare, making it difficult to train robust models. The largest public H2R dataset contains only 2,600 pairs across nine tasks, which is insufficient for learning generalizable skills.

  The paper's innovative entry point is to bypass these intermediate representations entirely and achieve end-to-end Human2Robot video generation directly. It proposes using a Diffusion Transformer for video In-Context Learning (ICL), leveraging powerful visual-temporal priors from large-scale pretrained video generation models and addressing data scarcity through an automatic paired-data synthesis pipeline.

2.2. Main Contributions / Findings

The paper makes several primary contributions:

1. Mitty, an End-to-End H2R Video Generation Framework: It proposes Mitty, the first end-to-end Human2Robot video generation framework built upon a Video Diffusion Transformer. This framework directly translates human demonstrations into robot actions without relying on explicit action labels or intermediate abstractions such as keypoints or trajectories, thus avoiding information loss and cumulative errors.

2. In-Context Learning for Consistency and Generalization: Technically, Mitty leverages In-Context Learning (ICL) to achieve both appearance and scene consistency and action consistency. This design significantly improves cross-task generalization, allowing the model to adapt to new tasks at inference time by simply conditioning on one human demonstration.

3. Efficient Data Synthesis and Mixed Training: The authors design an efficient automatic paired-data synthesis strategy that generates high-quality human-robot video pairs from large egocentric human activity datasets (like EPIC-Kitchens). This synthesized data is combined with existing datasets for mixed training, which markedly enhances the model's generalization ability on unseen tasks and environments.

   The key conclusions and findings are:

• Mitty significantly outperforms existing baselines in terms of generation quality, temporal coherence, and task success rates on both the Human2Robot and EPIC-Kitchens datasets.
• It demonstrates strong generalization to unseen tasks and environments, maintaining visual and action consistency.
• The first-frame conditioning mechanism consistently improves generation stability and fidelity.
• The bidirectional attention mechanism effectively fuses information between human condition tokens and robot denoise tokens, enabling robust cross-domain action translation.
• The automatic paired-data synthesis pipeline is critical for overcoming data scarcity and enabling the training of generalizable models, even if it requires human-in-the-loop filtering.
• The paper provides new insights for scalable robot learning from human observations by showing that high-fidelity H2R video generation can serve as a rich supervisory signal for future video-to-policy inversion.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To fully understand Mitty, a grasp of several fundamental concepts in deep learning, computer vision, and robotics is essential:

• Diffusion Models (DMs): These are a class of generative models that learn to reverse a gradual diffusion process that transforms data into random noise.

  • Forward Diffusion Process: In this process, a small amount of Gaussian noise is progressively added to an input data point (e.g., an image or video frame) over several time steps, eventually transforming it into pure Gaussian noise. This process is typically fixed and known.
  • Reverse Denoising Process: The model learns to reverse this process, i.e., it learns to predict and subtract the noise at each step, gradually transforming a noisy input back into a clean data sample. This is typically done by training a neural network (often a U-Net or Transformer) to predict the noise component.
  • Conditional Diffusion: Diffusion models can be conditioned on various inputs (e.g., text, images, or, in Mitty's case, human demonstration videos) to guide the generation process, producing outputs that adhere to the given conditions.
  • Why DMs are powerful: They excel at generating high-quality, diverse samples and are known for their strong visual-temporal priors when trained on large datasets.

• Transformers: Introduced in "Attention Is All You Need," Transformers are neural network architectures that rely heavily on self-attention mechanisms to process sequential data.

  • Self-Attention: This mechanism allows a model to weigh the importance of different parts of the input sequence when processing each element. For each element in a sequence (e.g., a word in a sentence, a patch in an image, or a frame in a video), self-attention calculates a weighted sum of all other elements, where the weights are determined by the similarity between the current element and all other elements.
  • Multi-Head Attention: This is an extension of self-attention where the attention mechanism is run multiple times in parallel, allowing the model to focus on different parts of the input from various "representation subspaces."
  • Encoder-Decoder Architecture: Transformers typically consist of an encoder (which processes the input sequence) and a decoder (which generates an output sequence).
  • Why Transformers are powerful: They are highly parallelizable, capable of capturing long-range dependencies, and have achieved state-of-the-art results in various domains, including natural language processing and computer vision.

• Diffusion Transformers: These combine the generative power of Diffusion Models with the sequence modeling capabilities of Transformers. Instead of using a U-Net (a common architecture in DMs), a Diffusion Transformer uses a Transformer-based architecture to predict the noise in the reverse diffusion process. This allows for better modeling of long-range dependencies and scaling to larger models.

• Variational Autoencoders (VAEs): These are generative models that learn a compressed, latent representation of input data.

  • Encoder: Maps input data (e.g., an image or video frame) into a latent space (a lower-dimensional representation). Instead of a single point, it maps to a distribution (mean and variance).
  • Decoder: Reconstructs the original data from a sample drawn from this latent distribution.
  • Reparameterization Trick: A technique used in VAEs to enable backpropagation through the sampling process.
  • Why VAEs are used: They are good at learning disentangled representations and are often used to convert high-dimensional data (like raw video pixels) into a compact, meaningful latent space suitable for processing by other models like diffusion models.

• In-Context Learning (ICL): A paradigm, popularized by large language models, where a model learns a new task by observing demonstrations provided directly in the input prompt (i.e., "in context"), without requiring any weight updates or fine-tuning.

  • Few-shot Learning: ICL often enables few-shot learning, meaning the model can perform a new task with only a few examples.
  • How it works: The model leverages its vast pre-trained knowledge to generalize from the provided in-context examples to solve the target task.
  • In Mitty's context: A human demonstration video serves as the "in-context" example, guiding the generation of the robot execution video.

• Robot Learning Concepts:

  • Human-to-Robot (H2R) Transfer: The process of transferring skills learned from human demonstrations to a robot.
  • Intermediate Representations: Abstract forms of data extracted from demonstrations, such as 3D keypoints (locations of specific body parts), trajectories (paths of movement), or depth maps (information about distance to objects).
  • Cross-Embodiment Transfer: Adapting skills between different physical forms (e.g., human hand to robot gripper), which often have different kinematics, degrees of freedom, and interaction capabilities.
  • End-to-End Learning: Training a system directly from raw input (e.g., human video) to desired output (e.g., robot video) without explicit intermediate processing stages.

3.2. Previous Works

The paper contextualizes Mitty within three main areas of prior research:

3.2.1. Video Generation Models

• Early GAN-based Approaches [33]: Generative Adversarial Networks (GANs) were among the first models for video generation. They consist of a generator (which creates fake data) and a discriminator (which tries to distinguish real from fake data), competing in a zero-sum game. While they showed promise, GANs often struggled with temporal consistency and mode collapse in video generation.
• UNet-based Approaches [13, 41, 53]: Many Diffusion Models initially used a U-Net architecture as the noise prediction network. A U-Net is a convolutional neural network with an encoder-decoder structure that includes skip connections, allowing it to efficiently capture both local and global features. These models improved video quality and temporal coherence over GANs.
• Modern Diffusion Transformer Architectures [18, 34, 47, 59]: This represents the current state-of-the-art. These models replace the U-Net with a Transformer architecture for noise prediction, enabling better handling of long-range dependencies and scalability. Wan 2.2 [47] (which Mitty builds upon) is an example of such a powerful video generation model pretrained on massive natural video corpora. These models can generate high-quality, temporally coherent videos conditioned on various inputs (text, images, multimodal).
• Applications: Recent studies also leverage large pretrained video generation models for robotics and mechanical manipulation [7], highlighting their potential for cross-domain generalization.

3.2.2. Learning from Human Videos

• Motivation: Using large human-centric video datasets (e.g., EPIC-Kitchens, Ego4D, EgoExo4D) to improve robot policy learning is appealing due to their scale and diversity, offering a more scalable alternative to costly teleoperation.
• Earlier Studies:
  • Visual Representation Extraction [4]: Learning features from human videos that are useful for robot tasks.
  • Reward Function Derivation [14]: Inferring reward signals for reinforcement learning from human demonstrations.
  • Motion Prior Estimation [35, 48]: Extracting general movement patterns from human videos to guide robot actions.
• Limitations of Earlier Approaches: Many still required additional robot data or specialized hardware (VR, hand-tracking devices), limiting scalability.
• Recent Progress: 3D hand pose estimation [5] allows extracting action information directly from RGB videos, but cross-embodiment transfer (human hand to robot arm) remains difficult. Humanoid robots can partially bridge this gap due to kinematic similarities.
• Mitty's Differentiation: Mitty aims to move beyond these limitations by achieving end-to-end generation directly from human demonstrations, without extracting intermediate representations like pose, trajectories, or depth. It explicitly leverages the fine-grained details in raw human videos.

3.2.3. In-Context Learning

• Foundation: In-Context Learning (ICL) [1, 3] has shown remarkable capabilities in adapting models to new tasks during inference time without explicit retraining. This was first popularized by large language models.
• Visual Generation Domain: ICL has been applied to high-quality image generation [9, 12, 15-17, 28, 40, 42-44, 49, 57, 58] and video generation [20, 55, 56]. These methods typically use reference images or videos as contextual cues.
• Robotics Domain: Preliminary studies [38] have explored ICL for visuomotor policies using teleoperation or simulation data.
• Limitations in Robotics ICL: These methods are often constrained by high data collection costs and limited task diversity, emphasizing the need for large, heterogeneous datasets for effective adaptation.
• Mitty's Application of ICL: Mitty adopts an ICL framework, built on the Wan 2.2 video diffusion model, to translate human demonstration videos into robot-arm executions, aiming for visual and action consistency throughout the generation process. By simply conditioning on one human demonstration, ICL can predict robot actions for novel tasks at test time without expensive retraining.

3.3. Technological Evolution

The field has evolved from heavily relying on explicit, engineered features and intermediate representations (e.g., keypoints, trajectories) to more end-to-end learning paradigms. Early attempts at H2R transfer were often pipeline-based, involving distinct stages of human pose estimation, pose mapping, and robot control. The advent of powerful deep learning models, particularly Transformers and Diffusion Models, revolutionized generative AI, enabling the synthesis of highly realistic and temporally coherent images and videos. This has allowed researchers to shift from inferring abstract action plans to directly generating visual outcomes.

Mitty's work fits within this timeline by pushing the boundaries of end-to-end learning in robotics. It capitalizes on the progress in large-scale video generation models and In-Context Learning to circumvent the limitations of intermediate representations, which were previously a necessary but error-prone bridge between human and robot embodiments. The automated data synthesis pipeline further represents an evolutionary step in overcoming the data scarcity bottleneck that has historically plagued robot learning.

3.4. Differentiation Analysis

Compared to the main methods in related work, Mitty's core differences and innovations are:

• Against Intermediate Representation Methods (e.g., methods using keypoints, trajectories, depth maps):

  • Core Difference: Mitty directly maps human video to robot video (end-to-end) without any explicit intermediate representations.
  • Innovation: This avoids information loss from abstraction and cumulative errors from multi-stage pipelines (e.g., Masquerade [23], as highlighted in the paper's comparison). It preserves fine-grained spatio-temporal dynamics from the raw human video, leading to higher temporal and visual consistency.

• Against General Video Generation Models (e.g., Wan 2.2, Kling, MoCha, Aleph):

  • Core Difference: Mitty is specifically adapted and trained for the $Human2Robot (H2R)$ translation task, leveraging video In-Context Learning and a specially curated human-robot paired dataset. General video editing models, while advanced, are not designed to maintain embodiment consistency and action alignment across domain shifts (human to robot) from a single reference image, as demonstrated by Mitty's superior performance in task success rate and embodiment consistency.
  • Innovation: Mitty leverages the strong visual-temporal priors of a pretrained Wan 2.2 model but fine-tunes it with a bidirectional attention mechanism to explicitly learn the complex mapping between human and robot actions and appearances, which generic models cannot achieve.

• Against Prior In-Context Learning (ICL) in Robotics:

  • Core Difference: While ICL has been explored for visuomotor policies in robotics, these often rely on teleoperation or simulation data and may not directly produce video outputs. Mitty applies ICL specifically for video-to-video translation in the H2R domain.

  • Innovation: Mitty's ICL is powered by a large-scale video generation backbone and is enabled by a robust paired-data synthesis pipeline, addressing the data scarcity issue that limits the effectiveness of ICL in previous robotic applications. This allows for cross-task and cross-environment generalization directly from human observation videos.

    In essence, Mitty innovates by combining end-to-end diffusion-based video generation with in-context learning for the challenging Human2Robot translation, supported by a scalable data synthesis pipeline, thereby offering a more direct, robust, and generalizable solution than prior approaches.

4. Methodology

4.1. Principles

Mitty formulates Human2Robot (H2R) video generation as a conditional denoising problem. The core idea is to train a Diffusion Transformer to learn the mapping from a human demonstration video $V^H$ to a robot execution video $V^R$ by predicting and removing noise from the robot video's latent representation, conditioned on the human video's latent representation. This process is end-to-end, meaning it avoids intermediate abstractions like keypoints or trajectories. The theoretical basis builds upon the success of large-scale pretrained video diffusion models (specifically Wan 2.2), which inherently possess strong visual and temporal priors. The intuition is that if a model can generate diverse, coherent videos, it can also learn to translate actions between related visual domains if properly conditioned. In-Context Learning (ICL) is employed to enable few-shot adaptation and generalization at inference time by treating the human demonstration as a contextual example.

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

Mitty's methodology can be broken down into its problem formulation, overall architecture, the detailed diffusion process with bidirectional attention, and the critical dataset construction pipeline.

4.2.1. Overall Architecture and Problem Formulation

The paper defines the objective as modeling the conditional distribution p _ { \theta } ( V ^ { R } | V ^ ^ { H } ), which captures fine-grained spatio-temporal correspondences between human actions and robot executions.

Mitty supports two primary settings:

1. H2R (Human2Robot Video Generation): The model directly generates a robot execution video from a human demonstration, starting without any initial robot frame. This is a zero-frame generation scenario.

2. HI2R (Human-and-Initial-Image-to-Robot Video Generation): This extends H2R by additionally providing an initial robot frame. This first-frame-conditioned generation setting helps define the robot's initial state, guiding its embodiment and motion planning.

   The entire framework is built upon Wan 2.2 [47], a state-of-the-art diffusion-based video generation model. Both human and robot videos are first compressed into latent tokens using the same Variational Autoencoder (VAE)-based video encoder. The human latents serve as clean conditioning tokens, while the robot latents are the denoising targets. These tokens are then concatenated along the temporal dimension and fed into a Diffusion Transformer. This Transformer is specifically enhanced with a bidirectional attention mechanism to facilitate information exchange between the human and robot modalities at each denoising step. This unified design ensures parameter sharing and prior leveraging across both H2R and HI2R settings, while offering flexible control.

The following figure (Figure 2 from the original paper) illustrates the overall architecture:

该图像是示意图，展示了Mitty模型在视频生成中的架构。左侧为源视频 $V_{src}$ 和目标视频 $V_{tar}$，右侧则描述了模型的结构，包括变分自编码器（VAE）、自注意力机制及其各组件（如学习的文本标记、噪声潜在标记和视频条件标记）。图中展示了不同类型标记的映射和注意力机制的关键部分。整体上，图像阐明了无须中间表示的端到端视频生成流程。 VLM Description: The image is a schematic illustrating the architecture of the Mitty model in video generation. On the left side are the source video $V_{src}$ and the target video $V_{tar}$, while the right side describes the model structure, including the variational autoencoder (VAE), self-attention mechanisms, and their components such as learned textual tokens, noise latent tokens, and video condition tokens. The image shows the mappings of different types of tokens and key parts of the attention mechanism. Overall, it clarifies the end-to-end video generation process without the need for intermediate representations.

4.2.2. Diffusion Process and Noise Injection

During training, the process involves progressively adding noise only to the robot latents, while the human latents are kept clean. This setup is crucial for modeling the conditional distribution $p _ { \theta } ( \mathbf { V } ^ { R } | \mathbf { V } ^ { H } )$.

Let $\mathbf { z } _ { 0 } ^ { R } = \mathrm { VAE } _ { \mathrm { enc } } ( \mathbf { V } ^ { R } )$ denote the clean latent representation of the robot video. The diffusion process adds noise to this latent representation over $T$ time steps:

$ \mathbf { x } _ { t } ^ { R } = \sqrt { \bar { \alpha } _ { t } } { \mathbf { z } } _ { 0 } ^ { R } + \sqrt { 1 - \bar { \alpha } _ { t } } \epsilon , \quad \epsilon \sim \mathcal { N } ( \mathbf { 0 } , \mathbf { I } ) . $

Where:

• $\mathbf { x } _ { t } ^ { R }$: The noisy latent representation of the robot video at time step $t$. This is the input to the Diffusion Transformer for denoising.

• $\mathbf { z } _ { 0 } ^ { R }$: The original, clean (noise-free) latent representation of the robot video, obtained by encoding the ground truth robot video $\mathbf { V } ^ { R }$ with the VAE encoder ($\mathrm { VAE } _ { \mathrm { enc } }$).

• $\bar { \alpha } _ { t }$: A scalar value representing the cumulative product of noise scale factors up to time step $t$. It controls the amount of original signal preserved (the $\sqrt { \bar { \alpha } _ { t } }$ term) and the amount of noise added (the $\sqrt { 1 - \bar { \alpha } _ { t } }$ term). As $t$ increases, $\bar { \alpha } _ { t }$ decreases, meaning more noise is added.

• $\epsilon$: A random noise vector sampled from a standard normal distribution $\mathcal { N } ( \mathbf { 0 } , \mathbf { I } )$, where $\mathbf { 0 }$ is a vector of zeros and $\mathbf { I }$ is the identity matrix. This means the noise is Gaussian with zero mean and unit variance.

  The cumulative noise schedule $\bar { \alpha } _ { t }$ is defined as:

$ \bar { \alpha } _ { t } = \prod _ { s = 1 } ^ { t } \alpha _ { s } , \quad t \in { 1 , \ldots , T } . $

Where:

• $\alpha _ { s }$: A scalar value at each time step $s$, typically close to 1, which determines the amount of noise added at that specific step. The product accumulates this noise.
• $T$: The total number of diffusion time steps.

4.2.3. Token Representation and Embeddings

Before being fed into the Diffusion Transformer, the latent representations are augmented with temporal and modality embeddings to provide contextual information.

Let $\mathbf { z } _ { 0 } ^ { H } = \mathrm { VAE } _ { \mathrm { enc } } ( \mathbf { V } ^ { H } )$ denote the clean latent representation of the human video. The tokens are formed as follows:

$ \mathbf { C } = \mathbf { z } _ { 0 } ^ { H } + \mathbf { E } _ { \mathrm { t i m e } } + \mathbf { E } _ { \mathrm { m o d } ( h ) } , $

$ \mathbf { D } = \mathbf { x } _ { t } ^ { R } + \mathbf { E } _ { \mathrm { t i m e } } + \mathbf { E } _ { \mathrm { m o d } ( r ) } . $

Where:

• $\mathbf { C }$: The condition tokens derived from the human demonstration video. These tokens remain noise-free throughout the diffusion process and provide the conditioning information.

• $\mathbf { z } _ { 0 } ^ { H }$: The original, clean latent representation of the human video.

• $\mathbf { E } _ { \mathrm { t i m e } }$: Temporal embeddings added to both human and robot tokens. These embeddings encode the position of each frame in the video sequence, allowing the model to understand temporal ordering and dynamics.

• $\mathbf { E } _ { \mathrm { m o d } ( h ) }$: Modality embedding specific to the human modality. This embedding signals to the model that these tokens originate from a human video.

• $\mathbf { D }$: The denoise tokens derived from the noisy robot video latent at time $t$. This is the part of the input that the Diffusion Transformer aims to denoise.

• $\mathbf { x } _ { t } ^ { R }$: The noisy latent representation of the robot video at time step $t$, as defined in the diffusion process.

• $\mathbf { E } _ { \mathrm { m o d } ( r ) }$: Modality embedding specific to the robot modality. This embedding signals that these tokens originate from a robot video.

  The dimension $d$ typically refers to the token or channel dimension within the Transformer architecture.

4.2.4. Bidirectional Attention Coupling

To achieve cross-domain video in-context learning, a key enhancement to the Diffusion Transformer is the bidirectional attention mechanism. This allows human-condition tokens ($\mathbf { C }$) and robot-denoise tokens ($\mathbf { D }$) to exchange information at each layer, enabling dynamic alignment of temporal cues, motion patterns, and object interactions across the two modalities. The mechanism uses row-wise softmax for computing attention weights.

The updated tokens, after bidirectional attention, are calculated as follows:

$ \tilde { \mathbf { C } } = \mathrm { Softmax } \Big ( \frac { \mathbf { C D } ^ { \top } } { \sqrt { d } } \Big ) \mathbf { D } , $

$ \tilde { \mathbf { D } } = \mathrm { Softmax } \Big ( \frac { \mathbf { D C } ^ { \top } } { \sqrt { d } } \Big ) \mathbf { C } . $

Where:

• $\tilde { \mathbf { C } }$: The updated human condition tokens, incorporating information from the robot denoise tokens.
• $\tilde { \mathbf { D } }$: The updated robot denoise tokens, incorporating information from the human condition tokens.
• $\mathbf { C D } ^ { \top }$: The dot product between the human condition tokens and the transpose of the robot denoise tokens. This calculates a similarity score between each human token and each robot token.
• $\mathbf { D C } ^ { \top }$: The dot product between the robot denoise tokens and the transpose of the human condition tokens.
• $\sqrt { d }$: A scaling factor (square root of the query/key dimension) used to stabilize gradients during training, a standard practice in Transformers.
• $\mathrm { Softmax } ( \cdot )$: The softmax function is applied row-wise to normalize the similarity scores into probability distributions, determining how much attention each token should pay to others.
• The updated tokens $[ \tilde { \mathbf { C } } ; \tilde { \mathbf { D } } ]$ are concatenated along the token dimension and fed to subsequent Transformer blocks, allowing a continuous flow of cross-modal information.

4.2.5. Denoising and Reverse Update

The Diffusion Transformer, taking the concatenated tokens and the current time step $t$, predicts the noise $\epsilon _ { \theta } ( \mathbf { x } _ { t } ^ { R } , \mathbf { C } , t )$ on the robot branch. This predicted noise is then used in the reverse diffusion process to estimate the less noisy latent state at the previous time step.

The reverse update step, which transforms $\mathbf { x } _ { t } ^ { R }$ to $\mathbf { x } _ { t - 1 } ^ { R }$, is given by:

$ { \displaystyle { \bf x } _ { t - 1 } ^ { R } = \frac { 1 } { \sqrt { \alpha _ { t } } } \bigg ( { \bf x } _ { t } ^ { R } - \frac { 1 - \alpha _ { t } } { \sqrt { 1 - \bar { \alpha } _ { t } } } \epsilon _ { \theta } ( { \bf x } _ { t } ^ { R } , { \bf C } , t ) \bigg ) + \sigma _ { t } { \bf z } , } $

$ { { \bf z } \sim \mathcal { N } ( { \bf 0 } , { \bf I } ) , } $

Where:

• ${ \bf x } _ { t - 1 } ^ { R }$: The estimated less noisy latent representation of the robot video at time step t-1.

• $\epsilon _ { \theta } ( { \bf x } _ { t } ^ { R } , { \bf C } , t )$: The noise predicted by the Diffusion Transformer model based on the current noisy robot latent, the human condition tokens, and the current time step.

• $\sigma _ { t }$: A scalar value determined by the variance schedule, controlling the amount of added noise during the reverse step. This term introduces stochasticity to ensure diversity in generated samples.

• ${ \bf z }$: A random noise vector sampled from a standard normal distribution $\mathcal { N } ( { \bf 0 } , { \bf I } )$.

  After iterating this reverse update process for $T$ steps, starting from pure noise, the model eventually obtains the estimated clean latent representation of the robot video, $\mathbf { z } _ { 0 } ^ { R }$. This latent representation is then passed through the VAE decoder to reconstruct the final robot execution video.

The final video is given by:

$ \hat { \mathbf { V } } ^ { R } = \mathrm { VAE } _ { \mathrm { dec } } ( \mathbf { z } _ { 0 } ^ { R } ) . $

Where:

• $\hat { \mathbf { V } } ^ { R }$: The generated robot execution video.

• $\mathrm { VAE } _ { \mathrm { dec } } ( \cdot )$: The VAE decoder function, which transforms the clean latent representation back into the pixel space of a video.

  This entire process models conditional generation without requiring explicit action labels, and it inherently supports both H2R (Zero-frame) and HI2R (First-frame conditioned) modes by controlling whether an initial robot frame is provided to $\mathbf { x } _ { t } ^ { R }$ at the start of the reverse diffusion.

4.2.6. Dataset Construction

A critical challenge in robot learning is the data bottleneck, specifically the scarcity of human-robot paired videos. Mitty addresses this by building upon the Masquerade [23] paper's data rendering approach and introducing an automated pipeline to synthesize high-quality paired videos from egocentric human activity datasets (like EPIC-Kitchens [6], Ego4D [10], and EgoExo4D [11]).

The following figure (Figure 3 from the original paper) illustrates the data synthesis pipeline:

VLM Description: The image is a diagram illustrating the process of generating robot videos from human demonstration videos, consisting of six steps: inputting a human demonstration video, detecting hands using Detectron 2, segmenting hands and arms using SAM 2, detecting hand keypoints, inpainting the removed hand regions, and rendering the robot arms.

The pipeline consists of the following steps:

1. Hand Pose Estimation:

   • Method: Utilizes models like HaMeR (for 3D Hand Mesh Recovery) to extract 3D hand keypoints and motion trajectories from the input egocentric human videos. These keypoints represent the spatial positions of various parts of the human hand.

2. Hand Segmentation and Removal:

   • Method: First, Detectron2 [50] is used to detect the bounding boxes of human hands. Then, Segment Anything 2 (SAM2) [36] is applied to perform fine-grained segmentation, precisely outlining and removing the human hands and forearms from the video frames.

3. Video Inpainting:

   • Method: After hand removal, the empty regions are filled using a video inpainting model such as E2FGVI [25]. This produces clean background videos where the human hands and forearms are seamlessly replaced by plausible background content, ensuring visual continuity across frames.

4. Pose Mapping:

   • Method: The predicted hand keypoints (from step 1) are mapped to robot end-effector poses. This involves translating human hand geometry and movement into corresponding robot gripper parameters.
   • Parameters mapped:
     • Target Position: Typically the midpoint between the thumb and index finger.
     • Target Orientation: Derived from the plane normal and a fitted vector, defining how the robot gripper should be oriented.
     • Gripper Opening: Determined by thresholding the distance between the thumb and index finger.

5. Robot Arm Rendering:

   • Method: Using a robotics simulation framework like RobotSuite [60], robot arms are rendered into the inpainted background videos at the mapped poses.
   • Refinement: Fine-tuning of poses and data cleaning/filtering are performed to improve the fidelity and physical plausibility of the rendered robot movements.

Mitigation of Cumulative Errors and Data Filtering: Given the multi-step nature of this automated pipeline, cumulative errors and inconsistencies can arise. To address this, a human-in-the-loop filtering mechanism is employed. This mechanism rigorously audits and removes low-quality samples, ensuring higher data fidelity and internal consistency. After filtering, each acceptable video is segmented into fixed-length clips, sampled at equal intervals, forming the final training and testing sets. This process yields a high-quality human-robot paired dataset that provides strong supervision for training In-Context Diffusion Transformer models like Mitty, crucial for cross-task and cross-environment generalization.

5. Experimental Setup

5.1. Datasets

Mitty is evaluated on two standardized datasets:

1. Human2Robot (H2R) Dataset:

   • Source & Characteristics: This is an existing dataset designed for human-to-robot tasks. It consists of human demonstration videos paired with corresponding robot execution videos.
   • Scale: After filtering short or low-quality sequences, the dataset yields 11,788 paired clips for training.
   • Splits: 500 clips are reserved for testing.
   • Temporal Resolution: All videos are resampled to 8 FPS (frames per second) and split into 41-frame clips.

2. EPIC-Kitchens Dataset:

   • Source & Characteristics: EPIC-Kitchens [6] is a large-scale egocentric human activity dataset featuring diverse household actions in various kitchen environments. The original dataset contains only human videos.

   • Synthesis Pipeline: The paper's automated synthesis pipeline (described in Section 4.2.6) is applied to EPIC-Kitchens videos to render robot arms, creating synthetic human-robot paired videos.

   • Scale: This process results in 34,820 synthesized clips.

   • Splits: 200 clips are used for testing, evenly divided into:

     • 100 seen scenes: Environments similar to those encountered during training.
     • 100 unseen scenes: Environments not present during training, specifically designed to evaluate cross-environment generalization.

   • Temporal Resolution: Similar to H2R, videos are resampled to 8 FPS and split into 41-frame clips.

     These datasets were chosen because H2R provides a direct benchmark for the task, while EPIC-Kitchens, augmented by the synthesis pipeline, offers a much larger and more diverse set of egocentric human actions in varied environments, allowing for robust evaluation of generalization capabilities.

5.2. Evaluation Metrics

The paper uses a comprehensive set of metrics to evaluate the quality, consistency, and task performance of the generated robot videos. For every evaluation metric, the following explanation structure is provided:

5.2.1. Fréchet Video Distance (FVD)

• Conceptual Definition: FVD is a metric used to evaluate the quality and diversity of generated videos. It measures the statistical distance between the feature representations of real (ground truth) videos and generated videos. Lower FVD indicates that the generated videos are more realistic, diverse, and consistent with the real video distribution. It captures both visual quality and temporal coherence.
• Mathematical Formula: FVD is an extension of Fréchet Inception Distance (FID) for images to video. It's calculated by embedding video clips into a feature space (e.g., using a pre-trained Inception-v3 network, or a 3D CNN for video features), then modeling these embeddings as multivariate Gaussian distributions. $ \mathrm{FVD} = ||\mu_1 - \mu_2||^2 + \mathrm{Tr}(\Sigma_1 + \Sigma_2 - 2(\Sigma_1 \Sigma_2)^{1/2}) $
• Symbol Explanation:
  • $\mu_1$: The mean of the feature vectors for the real video distribution.
  • $\mu_2$: The mean of the feature vectors for the generated video distribution.
  • $\Sigma_1$: The covariance matrix of the feature vectors for the real video distribution.
  • $\Sigma_2$: The covariance matrix of the feature vectors for the generated video distribution.
  • $||\cdot||^2$: The squared L2 norm (Euclidean distance) between the mean vectors.
  • $\mathrm{Tr}(\cdot)$: The trace of a matrix (sum of diagonal elements).

5.2.2. Peak Signal-to-Noise Ratio (PSNR)

• Conceptual Definition: PSNR is a common metric for quantifying the quality of reconstruction of an image or video compared to its original. It measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR values generally indicate better image/video quality.
• Mathematical Formula: $ \mathrm{PSNR} = 10 \cdot \log_{10} \left( \frac{\mathrm{MAX}_I^2}{\mathrm{MSE}} \right) $
• Symbol Explanation:
  • $\mathrm{MAX}_I$: The maximum possible pixel value of the image/video frames (e.g., 255 for 8-bit grayscale images).
  • $\mathrm{MSE}$: The Mean Squared Error between the original and reconstructed image/video.

5.2.3. Structural Similarity Index Measure (SSIM)

• Conceptual Definition: SSIM is a perceptual metric that quantifies the perceived quality of an image or video by measuring the similarity in luminance, contrast, and structure between two images/videos. Unlike PSNR, which is an absolute error metric, SSIM aims to reflect the human visual system's perception of quality. Values range from -1 to 1, where 1 indicates perfect structural similarity.
• Mathematical Formula: For two windows $x$ and $y$ of common size $N \times N$, the SSIM between them is: $ \mathrm{SSIM}(x,y) = \frac{(2\mu_x\mu_y + C_1)(2\sigma_{xy} + C_2)}{(\mu_x^2 + \mu_y^2 + C_1)(\sigma_x^2 + \sigma_y^2 + C_2)} $ The overall SSIM for an image is then calculated as the mean SSIM over various windows.
• Symbol Explanation:
  • $\mu_x$: The average of $x$.
  • $\mu_y$: The average of $y$.
  • $\sigma_x^2$: The variance of $x$.
  • $\sigma_y^2$: The variance of $y$.
  • $\sigma_{xy}$: The covariance of $x$ and $y$.
  • $C_1 = (K_1 L)^2$: A small constant to avoid division by zero, where $L$ is the dynamic range of the pixel values (e.g., 255) and $K_1 = 0.01$.
  • $C_2 = (K_2 L)^2$: A small constant to avoid division by zero, where $K_2 = 0.03$.

5.2.4. Mean Squared Error (MSE)

• Conceptual Definition: MSE measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. It is a common measure of the difference between values predicted by a model and the values actually observed. Lower MSE indicates better reconstruction fidelity.
• Mathematical Formula: For two images/videos $I$ and $K$ of size $m \times n$: $ \mathrm{MSE} = \frac{1}{mn} \sum_{i=0}^{m-1} \sum_{j=0}^{n-1} [I(i,j) - K(i,j)]^2 $
• Symbol Explanation:
  • m, n: Dimensions (height and width) of the image/video frames.
  • I(i,j): The pixel value at position (i,j) in the original image/video frame.
  • K(i,j): The pixel value at position (i,j) in the generated image/video frame.

5.2.5. Task Success Rate (SR)

• Conceptual Definition: SR quantifies the percentage of generated robot execution videos where the robot successfully completes the intended task as demonstrated by the human. It focuses on the functional correctness of the robot's actions rather than just visual fidelity.
• Measurement: This metric is derived from a structured human expert evaluation. Three domain experts independently review all generated videos, assigning a binary judgment (success or failure) for each clip. Disagreements are resolved through discussion to reach a consensus. A clip is a success if the robot correctly follows the human reference and completes the task, ignoring minor artifacts that don't affect task execution. A failure indicates action mismatches, implausible interactions, hovering/drifting, or task incompletion.

5.2.6. Embodiment Consistency

• Conceptual Definition: This metric evaluates whether the generated robot arm consistently matches the reference robot arm in its visual appearance and structural integrity throughout the video sequence. It assesses how well the model maintains the robot's identity and physical plausibility.
• Measurement: A normalized average score is computed using a combination of CLIP image score, DreamSim, and human evaluation.
  • CLIP image score: Measures similarity in a semantic embedding space.
  • DreamSim: A perceptual similarity metric.
  • Human evaluation: Subjective assessment by experts.

5.2.7. Human Preference

• Conceptual Definition: Human preference measures the subjective qualitative superiority of one method's output over others. It reflects user perception of overall video quality, realism, and aesthetic appeal.
• Measurement: All generated videos are anonymized. For each input, outputs from different methods are presented simultaneously to users, who are asked to select the video with the highest overall quality. The paper reports results from 20 valid survey responses.

5.3. Baselines

Mitty is compared against two groups of baselines:

1. Wan 2.2 Family Baselines (Internal Ablations and Scaling): These models are built on the Wan 2.2 backbone and trained using Mitty's in-context learning setup.

   • TI2V 5B (w/o 1st f): Mitty's architecture using the 5-billion parameter TI2V (Text-to-Image-to-Video) Wan 2.2 model, but without conditioning on the first robot frame (zero-frame generation).
   • TI2V 5B (w 1st f): Mitty's architecture using the 5-billion parameter TI2V Wan 2.2 model, with conditioning on the first robot frame (first-frame conditioned generation). This is the default baseline for TI2V-5B.
   • TI2V 14B: Mitty's architecture using the larger 14-billion parameter TI2V Wan 2.2 MoE (Mixture-of-Experts) model. This evaluates the impact of model scaling.
   • Ablations:
     • w/o ref vid. (without human reference video): The model predicts subsequent frames using only the initial robot frame and task description, effectively testing the impact of the human demonstration.
     • w/o task desc. (without task description): The model operates without any textual prompt, relying solely on visual information and first-frame conditioning (if applicable).
   • Training Strategy Comparisons:
     • Full (Mixed train.): The complete Mitty model (TI2V 5B, w/ 1st f, w/ task desc) trained jointly on both Human2Robot and EPIC-Kitchens datasets.
     • Full (Sep. train.): The complete Mitty model trained separately on each dataset. This is the default Mitty configuration used for direct comparisons with external baselines.

2. General-Purpose Video Editing Methods: These represent existing state-of-the-art video manipulation techniques.

   • Aleph [37] (Runway Aleph): A commercial video editing tool, likely based on diffusion models, that can perform image-conditioned editing.

   • Kling [21] (Kuaishou Kling): Another commercial video generation and editing API.

   • MoCha [46] (Orange Team Mocha): An open-source video character replacement method.

   • Shared characteristics: These methods typically take an input video and a single robot-arm reference image, often using prompts to guide the replacement of the human arm with the robot arm. Kling and MoCha specifically require a SAM-generated mask to specify the region of the arm to replace. These models are not specifically trained for H2R translation.

     The combination of these baselines allows for a comprehensive evaluation, comparing Mitty's specialized H2R approach against both its own architectural components (ablations), scaling factors, different training strategies, and general-purpose video manipulation tools.

5.4. Implementation Details

• Backbone: Pretrained Wan 2.2 TI2V-5B dense model and additionally the Wan 2.2 TI2V-14B MoE model.
• Fine-tuning Strategy: LoRA-based fine-tuning, adapting both high-noise and low-noise branches.
• Training Steps: 20k steps for TI2V-5B, 10k steps for TI2V-14B (due to computational cost).
• LoRA Rank: 96.
• Learning Rate: $1 \times 10^{-4}$.
• Hardware: Two H200 GPUs.
• Resolution: $416 \times 224$ for both training and inference.
• Batch Size: Effective batch size of 4.

6. Results & Analysis

6.1. Core Results Analysis

Mitty consistently achieves state-of-the-art results across various metrics and demonstrates strong generalization.

The following figure (Figure 4 from the original paper) shows Mitty's qualitative results on Human2Robot and EPIC-Kitchens:

VLM Description: The image is an illustration showing human gesture input, the robot's processing result, and the corresponding ground truth (GT). From left to right, the first row displays the input gestures, the second row shows the robot-generated results, and the third row presents the real targets for comparison, highlighting the application effectiveness of the Mitty model in video generation.

The qualitative results in Figure 4 and Figure 5 (in the supplementary materials, as shown below) demonstrate Mitty's ability to accurately preserve scene layout and object interactions while producing smooth, temporally coherent robot motions. Mitty also generalizes robustly to unseen tasks and environments, maintaining strong visual consistency, action consistency, and background stability.

The following figure (Figure 5 from the original paper) provides additional qualitative results on Human2Robot tasks:

VLM Description: The image is an illustration that showcases the robotic arm performing various tasks, including folding a dishcloth, writing with a brush, picking up blocks, stirring food, and cutting ingredients. Each task includes input, result, and ground truth (GT) to compare the generated video effects with the actual videos.

6.1.1. Quantitative Evaluation (Table 1 Analysis)

The following are the results from Table 1 of the original paper:

Analysis of Table 1:

• Impact of First-Frame Conditioning: Across both datasets (Human2Robot and EPIC-Kitchens, both Seen and Unseen environments), adding the first-frame condition (comparing w/o 1st f vs. w 1st f for TI2V 5B) consistently leads to:
  • Lower FVD and MSE: Indicating better overall video quality and less pixel-level error.
  • Slightly Higher PSNR and SSIM: Suggesting improved fidelity and structural similarity.
  • Higher Task Success Rate (SR): Demonstrating more stable and faithful task execution. For example, on Human2Robot, SR increases from 85% to 91%. On EPIC-Kitchens (Unseen), SR jumps from 79% to 86%. This highlights the importance of providing an initial state for guiding robot embodiment and motion planning, particularly in diverse and complex environments.
• Impact of Model Size (Scaling):
  • The larger T2V 14B model consistently achieves the best overall performance across all datasets and metrics when compared to $TI2V 5B (w 1st f)$. It yields the lowest FVD and MSE, and the highest PSNR, SSIM, and SR. For instance, on Human2Robot, T2V 14B achieves an FVD of 6.48 and SR of 93%, significantly outperforming the 5B model. This suggests that scaling up the Diffusion Transformer backbone, leveraging more parameters and potentially a Mixture-of-Experts (MoE) architecture, brings substantial benefits in generation quality and task success.
• Performance on EPIC-Kitchens vs. Human2Robot:
  • Performance metrics on EPIC-Kitchens are generally lower than on Human2Robot. For example, the best FVD on H2R is 6.48, while on EPIC-Kitchens (Seen) it's 6.90, and on EPIC-Kitchens (Unseen) it's 9.35. Similarly, PSNR and SSIM are notably lower. This reflects the increased difficulty of the EPIC-Kitchens dataset, characterized by more diverse scenes, complex environments, and moving camera viewpoints, making high-fidelity generation more challenging.
• Generalization to Unseen Environments:
  • While performance on EPIC-Kitchens (Unseen) is slightly worse than EPIC-Kitchens (Seen), the difference is not drastic, especially for the T2V 14B model (FVD 9.35 vs. 6.90, SR 89% vs. 90%). This demonstrates Mitty's strong generalization capabilities to previously unseen environments, which is a crucial aspect for scalable robot learning.

6.1.2. Comparison with Baselines (Table 3 Analysis)

The following are the results from Table 3 of the original paper:

Analysis of Table 3:

• Masquerade: This rendering-based pipeline achieves the highest embodiment consistency (96.5%) by a large margin. This is expected because the robot arm is directly composited into the scene based on pose mapping, meaning its appearance is perfectly stable. However, its task success rate (31.5%) and human preference (20.0%) are significantly lower. This is attributed to the heavy error accumulation across its multiple stages (e.g., hand segmentation, keypoint estimation, inpainting, rendering), leading to physically implausible robot motions or task failures despite perfect rendering of the robot arm itself.
• General Video Editing Methods (Kling, Mocha, Aleph): These methods generally show poor embodiment consistency (ranging from 60.2% to 77.4%) and very low human preference (3.2% to 4.8%). While they might achieve reasonable task success rates (70.0% to 78.0%), a single reference image is insufficient to maintain a stable robotic appearance and structure throughout the video sequence. This often results in deformation, structural errors, and distortions of the robot arm, highlighting their inadequacy for the specialized Human2Robot task.
• Mitty (Ours): Mitty achieves the best task success rate (84.5%) and human preference (68.0%), demonstrating its ability to generate functionally correct and aesthetically pleasing robot actions. It also achieves the second-highest embodiment consistency (92.6%), which is very close to Masquerade's score, significantly outperforming general video editing methods. This indicates that Mitty strikes a strong balance between correctness, visual fidelity, and structural stability of the robot arm. This is a crucial advantage, showing that the Diffusion Transformer with in-context learning can learn robust visual and action consistency without the explicit rendering pipeline's pitfalls.

6.1.3. Qualitative Comparison with Baselines

The following figure (Figure 6 from the original paper) compares Mitty with state-of-the-art video editing methods:

VLM Description: The image is a comparative illustration showing the performance of different methods (Kling, MoCha, Aleph, and Ours) in robot execution tasks. It displays human input and robotic reference images, with results generated by each method shown above and below, comparing the consistency of the robotic arm's structure and appearance throughout the task.

Figure 6 visually confirms the quantitative findings: general video editing models struggle to maintain the appearance and structural consistency of the robotic arm throughout the sequence, leading to deformation and structural errors. In contrast, Mitty, trained on paired data, consistently preserves the correct appearance and structure, reinforcing that Human2Robot remains a challenging task requiring dedicated research.

The following figure (Figure 5 from the original paper's supplementary material, labeled as Figure 6 in the main paper, but the caption refers to "Figure 5" so I'll follow that) provides a qualitative comparison between Mitty and Masquerade:

VLM Description: The image is an illustration that shows the input video and its generated results under different models. The top section displays comparisons between the input and the outputs of two models (Masquerade and Ours), highlighting potential errors such as joint detection and abnormal behavior. The bottom section shows another set of inputs and generated results, illustrating the differences in reliability of robot action execution between the models.

Figure 6 (labeled as Figure 5 in the paper) highlights Masquerade's multi-stage pipeline issues, such as joint detection failures, inpainting errors, and incorrect robot arm rendering (e.g., penetration or floating), which compound and degrade visual quality and physical realism. Mitty's end-to-end model, trained on curated data, produces more reliable Human2Robot mappings.

6.2. Ablation Study (Table 2 Analysis)

The following are the results from Table 2 of the original paper:

Analysis of Table 2 (using TI2V-5B model with first-frame conditioning as default for Full (Sep. train.)):

• Impact of Human Reference Video (w/o ref vid.):
  • Removing the human reference video leads to a significant degradation across all metrics on both datasets. For Human2Robot, SR drops from 91% (Full (Sep. train.)) to 65% (w/o ref vid.). For EPIC-Kitchens (Seen), SR drops from 88% to 75%.
  • This is particularly severe on EPIC-Kitchens due to its diverse scenes and moving camera viewpoints. This strong degradation emphasizes that Mitty heavily relies on the visual demonstration from the human video for in-context learning and action consistency. The model cannot infer the complex actions from just an initial robot frame and a task description alone.
• Impact of Task Description (w/o task desc.):
  • Removing the task description prompts causes only minor changes in performance compared to removing the human reference video. For Human2Robot, SR decreases slightly from 91% to 88%. For EPIC-Kitchens (Seen), SR drops from 88% to 83%.
  • This indicates that Mitty relies more heavily on visual demonstrations than on textual cues for action translation. While textual prompts provide some guidance, the visual information from the human demonstration video is the dominant conditioning factor.
• Separate vs. Mixed Training:
  • The Full model trained separately on each dataset generally outperforms the Full model trained with mixed training. For Human2Robot, separate training achieves an SR of 91% compared to 72% for mixed training. For EPIC-Kitchens (Seen), separate training achieves an SR of 88% compared to 81% for mixed training.
  • This suggests that while the synthetic data pipeline helps in data scarcity, the datasets (Human2Robot and EPIC-Kitchens) might be sufficiently distinct in their tasks and environments (e.g., single-arm vs. dual-arm manipulation, scene complexity) that a specialized model for each dataset currently yields better results. The benefits of domain-specific training outweigh the generalization benefits of mixed training with the current data distribution and model capacity.

6.3. Additional Experimental Results

Additional qualitative results are provided in the supplementary materials, reinforcing the main findings.

The following figure (Figure 8 from the original paper's supplementary material) shows more qualitative results on Human2Robot tasks:

VLM Description: The image is an illustration showing multiple pairs of human actions as input and the corresponding robot execution results. In each row, the left side displays the human operation video input, while the right side presents the generated robot execution video, transformed by the Mitty model.

Figure 8 (Supplementary Material) shows that Mitty consistently maintains scene structure, motion coherence, and identity stability across a range of tasks, including grasping, placing, cooking-related actions, countertop manipulation, and basic tool usage.

The following figure (Figure 9 from the original paper's supplementary material) compares Mitty with Masquerade on EPIC-Kitchens dataset:

VLM Description: The image is an illustration showing the comparison between input videos and generated results. Each set of three columns represents the input, the output from the Masquerade method, and the output from our model. These images highlight issues like 'inpainting failure' and 'unreasonable interaction' in various examples, emphasizing the advantages of our approach in video generation.

Figure 9 (Supplementary Material) provides additional examples on EPIC-Kitchens, demonstrating Mitty's robustness to camera shake, complex scenes, lighting variations, and multi-object interactions. It further highlights Masquerade's failure patterns, such as hovering artifacts, black-frame outputs, arm distortion, inpainting errors, and incorrect gripper orientations, reinforcing Mitty's stability.

6.4. Our Failure Case Analysis

The following figure (Figure 10 from the original paper's supplementary material) illustrates Mitty's failure cases:

VLM Description: The image is a comparison diagram showing the contrast between the input and generated results. The left side displays the original input image, while the right side shows our generated results, highlighting three different scenarios: erasing failures, robot arm structural distortion, and unreasonable interaction.

Figure 10 (Supplementary Material) categorizes Mitty's failure modes into three types:

1. Erasing Failures: Regions that should have been replaced or removed are not fully erased, leading to incomplete transitions or visual remnants from the source video. This suggests limitations in the in-context masking or inpainting capabilities within the diffusion process.

2. Robot Arm Structural Distortion: The generated robot arm exhibits geometric inconsistencies, unnatural joint angles, or anatomically impossible shapes. This is noted as the most frequent failure mode on the EPIC-Kitchens dataset, likely due to complex hand-object interactions and challenging first-person motion patterns that make it difficult for the model to learn perfectly consistent robot kinematics.

3. Unreasonable Interaction: The robot arm's motion does not follow physically plausible trajectories or fails to maintain correct contact with manipulated objects. Examples include missing the target, drifting past the object, or interacting with nonexistent items. This points to limitations in the model's understanding of physics, common-sense reasoning, or precise object affordances.

   These failures highlight areas for future improvement, particularly in handling complex kinematics, physical interactions, and achieving perfect visual consistency during erasure and rendering.

7. Conclusion & Reflections

7.1. Conclusion Summary

In this work, the authors introduced Mitty, a pioneering Diffusion Transformer framework for end-to-end Human2Robot (H2R) video generation. Mitty leverages in-context learning and is built upon the powerful Wan 2.2 video diffusion model backbone. A key innovation is bypassing traditional intermediate representations like keypoints or trajectories, directly translating human demonstration videos into temporally aligned robot-execution videos. To address the critical issue of paired-data scarcity, the paper also presented a scalable automatic synthesis pipeline for generating high-quality human-robot video pairs from large egocentric datasets. Extensive experiments on the Human2Robot dataset and synthesized EPIC-Kitchens dataset demonstrated Mitty's superior performance, achieving state-of-the-art results, strong generalization to unseen tasks and environments, and clear advantages over both multi-stage rendering pipelines (like Masquerade) and general-purpose video editing systems. Ablation studies further underscored the effectiveness of in-context conditioning (especially the human reference video) and highlighted the nuanced impact of different training strategies. Mitty provides a meaningful starting point for future video-to-policy research, paving the way for more scalable and generalizable robot learning from human observations.

7.2. Limitations & Future Work

The authors openly acknowledge several limitations of the current Mitty framework and propose future research directions:

• Not a Complete Video Policy Pipeline: Mitty currently focuses on generating high-fidelity robot-arm execution videos. It does not explicitly output control-ready action sequences (e.g., joint angles, gripper forces). Therefore, it has not been evaluated in a full closed-loop setting on real robots.

• Expert-Based Task Success Rate: The task success rate is based on human expert assessments of the generated videos, rather than physical rollouts on real robots or simulations. This means the success is a visual judgment of plausibility rather than a verifiable robotic action.

  The proposed future work includes:

• Incorporating Action or Policy Prediction: Extending the framework to directly predict executable control signals or policies.

• Closed-Loop Experiments: Conducting experiments in both simulation and on real hardware to validate the generated policies in a physical context.

• Automated and Physically Grounded Evaluation Metrics: Developing more objective and robot-centric metrics that go beyond visual fidelity to assess the physical correctness and effectiveness of the robot's actions.

• Advancing H2R to a Complete Video Policy Solution: Ultimately, the goal is to fully bridge the gap from human observation videos to robot control.

• Exploring More Complex Tasks and Tighter Human-Robot Mapping: Applying the framework to even more challenging manipulation scenarios and improving the precision of the cross-embodiment translation.

7.3. Personal Insights & Critique

Mitty represents a significant leap forward in the field of Human2Robot (H2R) learning. The shift to an end-to-end video-to-video generation approach, bypassing intermediate representations, is conceptually elegant and addresses a long-standing challenge of cumulative errors and information loss. The integration of Diffusion Transformers with In-Context Learning (ICL) is particularly powerful, leveraging the strong visual priors of large generative models to achieve impressive visual and temporal consistency.

Inspirations and Transferability:

• Scalable Data Generation: The automatic paired-data synthesis pipeline is a critical contribution. The data bottleneck is a major impediment in many robotic applications, and a robust pipeline to generate diverse, high-quality synthetic training data from readily available human videos is a game-changer. This approach could be adapted to other domains where paired data is scarce but unpaired domain-specific data is abundant (e.g., generating medical images from sketches, or simulating complex fluid dynamics from simpler inputs).
• Video-to-Policy Bridge: The idea that high-fidelity video generation can serve as "structured supervisory signals" for future video-to-policy inversion is profound. Instead of learning sparse keypoints, a robot could potentially learn from dense, visually rich demonstrations of "what success looks like," which is much closer to human intuition. This could simplify policy learning and lead to more robust, generalizable behaviors.
• Generalization through Large Models: The strong performance of the Wan 2.2 backbone highlights the power of pre-trained large models in robotics. Leveraging these foundational models, rather than training from scratch, is a promising avenue for accelerating progress across various robotic tasks.

Potential Issues, Unverified Assumptions, and Areas for Improvement:

• Gap to Real-World Control: The biggest limitation, as acknowledged by the authors, is the gap between video generation and actual robot control. While the generated videos are visually convincing, the exact kinematic and dynamic constraints of a real robot are not explicitly modeled or guaranteed. Translating pixel-level video into precise joint commands or end-effector forces is non-trivial and often involves complex inverse kinematics/dynamics. The unreasonable interaction failure mode points directly to this lack of physical grounding.

• Robustness of Data Synthesis Pipeline: While the human-in-the-loop filtering step improves data quality, the upstream components (HaMeR, Detectron2, SAM2, E2FGVI, RobotSuite) still introduce potential errors. The quality of the synthesized data directly impacts the training. Further research could focus on making this synthesis pipeline more robust and entirely automated, perhaps even using a generative model to directly synthesize paired data instead of a multi-stage rendering process. The "Masquerade failure cases" in the supplementary material underscore these challenges.

• Embodiment Transfer for Complex Objects: While the robot arm is well-maintained, how well embodiment consistency and interaction fidelity would hold for more complex robot bodies (e.g., humanoid hands with multiple fingers) or highly deformable objects is an open question.

• Interpretability and Debugging: Diffusion models, especially large Transformer-based ones, can be black boxes. When a robot performs an unreasonable interaction or exhibits structural distortion, pinpointing the exact cause in the latent space or attention mechanisms can be challenging, complicating debugging and refinement.

• Computational Cost: Training and inference with large Diffusion Transformers (e.g., TI2V 14B) are computationally expensive, requiring specialized hardware (H200 GPUs). This might limit widespread adoption or real-time applications unless more efficient architectures or distillation techniques are developed.

• Mixed Training Performance: The finding that separate training outperforms mixed training suggests that while the synthetic data is useful, there might still be domain gaps or task specificities between the Human2Robot dataset and the synthesized EPIC-Kitchens data that the model struggles to reconcile during joint training. A more sophisticated domain adaptation or multi-task learning strategy might be beneficial here.

  Overall, Mitty presents a compelling vision for Human2Robot learning by creatively combining advanced generative models with a robust data strategy. Its impact is likely to be significant, propelling the field closer to truly generalizable and scalable robotic skill acquisition from human observation.