1. Bibliographic Information

• Title: CoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Driving
• Authors: Yishen Ji, Ziyue Zhu, Zhenxin Zhu, Kaixin Xiong, Ming Lu, Zhiqi Li, Lijun Zhou, Haiyang Sun, Bing Wang, Tong Lu.
• Affiliations: The authors are from Nanjing University, Xiaomi EV, Nankai University, and Peking University, indicating a strong collaboration between academic institutions and industry (specifically, an electric vehicle company).
• Journal/Conference: The paper is available on arXiv, which is a preprint server. This means it has not yet undergone formal peer review for a conference or journal, though it represents cutting-edge research. The arXiv ID is 2503.22231v2, which seems to indicate a future publication date; as of now, this is a preprint.
• Publication Year: The paper was submitted to arXiv, and the version cited is v2. The ID suggests a potential submission date in March 2025.
• Abstract: The paper addresses the challenge of generating controllable, multi-view driving videos that are both photorealistic and 3D consistent. Current methods, which use 2D conditions like HD maps, often fail to achieve this consistency. The proposed solution, CoGen, is a framework that first generates high-quality, controllable 3D semantic representations of driving scenes. These 3D conditions, which are more detailed than 2D layouts, are then used to guide the video generation process, significantly improving spatial consistency. Additionally, a Consistency Adapter module is introduced to make the model more robust to handling multiple control signals. The results show that CoGen excels in creating realistic videos with strong geometric fidelity, making it a promising tool for generating training data for autonomous driving systems.
• Original Source Link:
  • ArXiv: https://arxiv.org/abs/2503.22231v2
  • PDF: https://arxiv.org/pdf/2503.22231v2.pdf
  • Status: Preprint.

2. Executive Summary

• Background & Motivation (Why):

  • Core Problem: Modern autonomous driving systems require vast amounts of diverse training data, especially for rare or dangerous "corner case" scenarios. Real-world data collection is expensive, time-consuming, and often fails to capture these rare events. Generative models offer a scalable solution by synthesizing training data, but existing methods for driving video generation struggle with a critical requirement: 3D consistency. When generating videos from multiple camera viewpoints (e.g., front, side, and rear cameras on a car), the generated world must be geometrically coherent. Objects must have a consistent shape, size, and position across all views and over time.
  • Existing Gaps: Prior work often relies on coarse 2D conditions like bird's-eye-view (BEV) maps or 2D bounding boxes. These conditions suffer from two main issues:
    1. Geometric Simplification: They fail to capture the fine-grained 3D structure of the scene (e.g., building facades, vehicle shapes), leading to a lack of realism.
    2. Projection Inconsistency: Projecting 3D scenes into 2D conditions and then back into multi-view 2D images can introduce artifacts, such as floating cars or misaligned objects.
  • Innovation: CoGen tackles this by flipping the paradigm. Instead of generating video directly from coarse 2D conditions, it first generates a temporally consistent 3D semantic model of the scene. This 3D model, rich in geometric and semantic detail, is then projected into multiple fine-grained 2D guidance maps, which serve as much stronger conditions for the video generation model.

• Main Contributions / Findings (What):

  1. 3D-Semantics-Derived Guidance: The paper systematically investigates using 3D semantic representations as the primary condition for video generation. It proposes generating and using four types of 2D projections from these 3D models—Semantic Map, Depth Map, Coordinate Map, and Multi-Plane Image (MPI)—which provide superior geometric and contextual guidance compared to traditional 2D layouts.
  2. Consistency Adapter: A lightweight adapter module is introduced to improve the model's ability to handle multiple, diverse conditions. This adapter helps ensure smooth motion and temporal coherence in the generated videos without needing to retrain the entire large-scale diffusion model.
  3. State-of-the-Art Performance: CoGen achieves new state-of-the-art results on the nuScenes dataset for driving video generation, significantly outperforming previous methods in both visual quality (measured by FVD) and controllability (measured by performance on downstream tasks like 3D object detection and BEV segmentation).
  4. Foreground-Aware Loss: A specialized Mask Loss is introduced to focus the model's learning on foreground objects (like vehicles and pedestrians), which are perceptually critical but often occupy a small area of the image, leading to sharper and more detailed results.

3. Prerequisite Knowledge & Related Work

• Foundational Concepts:

  • Diffusion Models: These are a class of generative models that learn to create data by reversing a gradual noising process. They start with a real data sample (like an image), add Gaussian noise step-by-step until it becomes pure noise, and then train a neural network to denoise it back to the original sample. To generate new data, the model starts from random noise and applies this learned denoising process.
  • Latent Diffusion Models (LDMs): Training diffusion models directly on high-resolution images is computationally very expensive. LDMs, popularized by Stable Diffusion, address this by first encoding the image into a much smaller, lower-dimensional "latent space" using an autoencoder. The diffusion process then happens in this compact latent space, making it far more efficient. The final denoised latent is then decoded back into a high-resolution image.
  • ControlNet: This is an architectural extension for diffusion models that allows for fine-grained spatial control over the generation process. It takes an additional conditioning input (e.g., a Canny edge map, a human pose skeleton, or a semantic map) and uses it to guide the diffusion model. A ControlNet is a trainable copy of the diffusion model's encoder blocks, which learns to inject the control signal into the generation process while preserving the knowledge of the original pretrained model.
  • Bird's-Eye-View (BEV): In autonomous driving, BEV is a common representation that projects sensor data (from cameras, LiDAR, etc.) onto a 2D grid as if viewed from directly above the vehicle. It provides a unified spatial layout of the surrounding environment, including roads, lanes, and other agents.

• Previous Works:

  • Early methods like BEVGen and MagicDrive used BEV layouts (HD maps, bounding boxes) as conditions to generate street-view images or videos. While they provided some level of control, they suffered from the "geometric simplification" problem mentioned earlier.
  • Models like DriveDreamer and UniMLVG extended this to multi-view video generation, but still relied on 2D layouts and struggled with "projection inconsistency," where objects might appear correctly in one view but be distorted or misplaced in another.
  • Some recent works like UniScene began using 3D "occupancy" (a 3D grid indicating which voxels are occupied by objects) as an intermediate representation to improve consistency. CoGen builds on this idea but goes further by generating a full 3D semantic model (where each voxel has a class label) and exploring multiple ways to project this rich information for guidance.

• Differentiation: CoGen's primary innovation is its 3D-first approach. While others use 2D conditions to generate 3D-inconsistent videos, CoGen first synthesizes a high-fidelity, temporally consistent 3D semantic scene. This 3D representation becomes the "source of truth" from which all multi-view 2D video frames are conditioned, inherently enforcing 3D consistency. The use of multiple complementary projections (Semantic Map, Depth Map, etc.) and the Consistency Adapter further strengthens this framework, making it more robust and effective than prior art.

4. Methodology (Core Technology & Implementation)

The core of CoGen's methodology is a two-stage process: first, generate high-quality temporal 3D conditions, and second, use these conditions to guide a powerful video diffusion model.

该图像是论文CoGen中用于3D一致性视频生成的核心框架示意图，包括训练推理流程（a）、3D语义的射线投影模块（b）及扩散变换器结构（c），展示了如何结合多条件控制实现高质量视频生成。

The image above (Figure 1 in the paper) provides a high-level overview of the CoGen framework.

• (a) Training and Inference Pipeline: The overall flow shows that input videos are encoded into a latent space. A separate process generates 3D semantics, which are projected into guidance conditions via ray-casting. These conditions, along with the noisy latent video, are fed into a Diffusion Transformer. A Foreground Mask, also derived from the 3D semantics, is used to compute a special loss (L_Mask) that focuses on important objects. The transformer predicts the noise, which is subtracted to denoise the latent video, and a VAE decoder finally produces the generated video.
• (b) 3D Semantics Ray-Casting Projection: This part details how the 3D semantic grid is converted into 2D guidance maps. It shows different projections (Semantic Map, Depth Map, MPI, Coordinate Map) being generated and then encoded. These are grouped and fused before being fed into the control network.
• (c) Diffusion Transformer: This shows the architecture of the main generative model. It's a DiT (Diffusion Transformer) with Control Blocks (similar to ControlNet) to incorporate the guidance signals. The novel Consistency Adapter is inserted between the control and base blocks to improve temporal coherence.

4.1. Preliminaries: Conditional Diffusion Models

The paper builds on Latent Diffusion Models (LDMs) and ControlNets. The forward process adds noise to a latent representation $z_0$ over $t$ timesteps: $$q ( \mathbf { z } _ { t } | \mathbf { z } _ { 0 } ) = \mathcal { N } \Big ( \mathbf { z } _ { t } ; \sqrt { \bar { \alpha } _ { t } } \mathbf { z } _ { 0 } , ( 1 - \bar { \alpha } _ { t } ) \mathbf { I } \Big )$$

• $z_t$: The noisy latent at timestep $t$.

• $z_0$: The original, clean latent representation of an image.

• $\bar{\alpha}_t$: A cumulative product of variance schedule parameters, controlling the noise level. $\mathcal{N}$ denotes a Normal (Gaussian) distribution.

  The reverse process is a neural network $\epsilon_{\theta}$ trained to predict the noise that was added. When a ControlNet is used, this process is conditioned on an external signal $c$: $$p _ { \theta } ( \mathbf { z } _ { t - 1 } \vert \mathbf { z } _ { t } , \mathbf { c } ) = \mathcal { N } \Big ( \mathbf { z } _ { t - 1 } ; \mu _ { \theta } ( \mathbf { z } _ { t } , t , \mathbf { c } ) , \boldsymbol { \Sigma } _ { \theta } ( \mathbf { z } _ { t } , t , \mathbf { c } ) \Big )$$

• $c$: The conditioning signal (e.g., semantic map, depth map).

• $\mu_{\theta}$ and $\Sigma_{\theta}$: The mean and variance of the denoised distribution, predicted by the network.

4.2. Temporal 3D Semantics Conditions Generator

Instead of using simple 2D maps, CoGen first generates a 3D semantic representation of the scene over time. This is a 4D tensor $\mathbf{S} \in \mathbb{R}^{T \times H \times W \times D}$, where each voxel (x, y, z) at time $t$ has a semantic label (e.g., car, road, building).

This generator is itself a diffusion model trained on 3D semantic data. It uses a 3D VAE to tokenize the 3D semantics and a diffusion transformer to learn the distribution, conditioned on standard 2D maps and bounding boxes.

Once a temporal 3D semantic sequence is generated, it is projected into four types of 2D guidance maps using ray-casting for each camera view.

该图像是图2的示意图，展示了用于视频生成的多种三维语义条件，包括真实场景、语义图、深度图、多平面图像（MPI）、坐标图和前景掩码，体现了通过光线投射将3D语义网格投影到相机视角以捕获几何和语义信息的过程。

As shown in Figure 2 above, these conditions provide rich, multi-faceted information:

1. Semantic Map: A 2D map where each pixel's color corresponds to the semantic label of the first 3D voxel its ray hits. This provides object class information.
2. Depth Map: A grayscale image where each pixel's intensity represents the distance to the first 3D voxel its ray hits. This provides explicit geometric structure.
3. Coordinate Map: An RGB image where the R, G, B channels encode the 3D world coordinates (X, Y, Z) of the first hit voxel. This provides absolute 3D positional information.
4. Multi-Plane Image (MPI): A more complex representation that captures color and opacity on several discrete depth planes. This helps model semi-transparent objects and provides more depth context than a simple depth map.

4.3. 3D Geometry-Aware Diffusion Transformer

The main video generation model is a Diffusion Transformer (DiT). It operates on latent video clips and is enhanced with control mechanisms.

• Architecture: The model uses a transformer architecture, which is effective at capturing long-range dependencies in data. It is modified with a spatial view-inflated attention mechanism to ensure consistency across different camera views.
• 3D Guidance Control Encoder: To feed the four new 3D-derived conditions into the model, a specialized encoder is used (see Figure 1b). The Semantic, Depth, and Coordinate Maps are encoded using the same VAE as the RGB images to ensure they live in a compatible latent space. The MPI is encoded with a separate, lightweight convolutional encoder. The features from these conditions are then grouped, fused with 1x1 convolutions, and fed into the Control Blocks of the DiT.

4.4. Multi-Condition Consistency Adapter

To improve the model's ability to adapt to these new, complex conditions without costly retraining, a lightweight Consistency Adapter is introduced.

该图像是论文中图3的示意图，展示了一致性适配器的架构流程。图中包含时序嵌入模块，空间卷积、时序卷积及时序自注意力模块，输入控制条件$c$，经过一系列卷积和归一化变换，输出替代控制条件$c'$。

As shown in Figure 3, the adapter is a small neural network inserted between the Control Blocks and the main Base Blocks of the DiT. It consists of:

• A Spatial Convolution module to process features within each frame.

• A Temporal Convolution module (using 3D convolutions) to smooth features across time.

• A Temporal Attention module to capture long-range temporal dependencies.

  This adapter is fine-tuned after the main model is trained, allowing for efficient adaptation and significantly improving temporal smoothness and motion coherence in the final videos.

4.5. Training and Inference with Foreground-Mask Loss

Foreground objects like cars and pedestrians are the most important elements in a driving scene but often occupy only a small fraction of the pixels. A standard Mean Squared Error (MSE) loss would treat all pixels equally, potentially leading to blurry or inaccurate foregrounds.

To fix this, CoGen introduces a foreground-aware mask loss. A binary mask $\mathbf{M}$ is created from the 3D semantics, where pixels corresponding to foreground objects are set to 1. The diffusion loss is then modified to add an extra penalty for errors on these foreground pixels: $$\mathcal { L } = \mathbb { E } \left[ \Vert e \Vert _ { 2 } ^ { 2 } + \gamma \Vert \mathbf { M } \odot e \Vert _ { 2 } ^ { 2 } \right] , \quad e = \epsilon - \epsilon _ { \theta } ( z _ { t } , t , c )$$

• $e$: The error between the true noise $\epsilon$ and the predicted noise $\epsilon_{\theta}$.

• $\mathbf{M}$: The binary foreground mask.

• $\odot$: Element-wise multiplication.

• $\gamma$: A hyperparameter that weights the importance of the foreground.

  This forces the model to pay more attention to getting the details of cars, pedestrians, and other critical objects right.

5. Experimental Setup

• Datasets: The experiments are conducted on nuScenes, a large-scale, publicly available dataset for autonomous driving research. It contains 1000 driving scenes, each about 20 seconds long, with multi-camera video and rich annotations. The original 2Hz annotations were interpolated to 12Hz for smoother video generation.
• Evaluation Metrics: The paper evaluates performance from two angles: generation quality and controllability.
  1. Fréchet Video Distance (FVD):
     • Conceptual Definition: FVD is the video equivalent of FID. It measures the statistical similarity between the distribution of generated videos and real videos. It computes the "distance" between features extracted from both sets of videos using a pretrained video classification network. A lower FVD score indicates that the generated videos are more realistic in terms of appearance, motion, and temporal dynamics.
     • Mathematical Formula: $$\text{FVD}(x, g) = d^2((\mu_x, \Sigma_x), (\mu_g, \Sigma_g)) = \|\mu_x - \mu_g\|_2^2 + \text{Tr}(\Sigma_x + \Sigma_g - 2(\Sigma_x\Sigma_g)^{1/2})$$
     • Symbol Explanation: $x$ and $g$ represent the sets of real and generated videos. $\mu_x, \mu_g$ are the mean feature vectors, and $\Sigma_x, \Sigma_g$ are the covariance matrices of the features extracted by a pretrained 3D CNN (like I3D). $\text{Tr}(\cdot)$ denotes the trace of a matrix.
  2. Fréchet Inception Distance (FID):
     • Conceptual Definition: FID measures the quality and diversity of generated images (frames, in this case) compared to real images. It uses features from a pretrained InceptionV3 network. A lower FID score means the generated images are closer to the real data distribution.
     • Mathematical Formula: The formula is identical to FVD's, but the features are extracted from individual images using a 2D CNN (InceptionV3).
  3. mean Intersection over Union (mIoU):
     • Conceptual Definition: Used to evaluate the controllability of BEV segmentation. The generated videos are fed into a perception model (BEVFormer) to produce a BEV semantic map. This map is then compared to the ground-truth map. mIoU calculates the average IoU over all semantic classes, where IoU for a single class is the area of overlap divided by the area of union between the predicted and true regions. A higher mIoU means the generated video content (road layout, etc.) is more accurately controlled.
     • Mathematical Formula: $$\text{mIoU} = \frac{1}{C} \sum_{i=1}^{C} \frac{\text{TP}_i}{\text{TP}_i + \text{FP}_i + \text{FN}_i}$$
     • Symbol Explanation: $C$ is the number of classes. $\text{TP}_i, \text{FP}_i, \text{FN}_i$ are the number of true positive, false positive, and false negative pixels for class $i$, respectively.
  4. mean Average Precision (mAP):
     • Conceptual Definition: Used to evaluate controllability for 3D object detection. The generated videos are fed into a 3D object detector (BEVFormer), and the predicted 3D bounding boxes are compared against the ground truth. mAP is the mean of the Average Precision (AP) scores across all object classes. AP summarizes the precision-recall curve, providing a single score for detection quality per class. Higher mAP indicates that the generated objects (vehicles, pedestrians) are more accurately placed and shaped according to the input conditions.
     • Mathematical Formula: There isn't a single simple formula; it's a procedural metric based on matching detections to ground truth objects and calculating precision/recall at various confidence thresholds.
• Baselines: The main baseline is DiVE, a strong recent model that also uses a DiT architecture but relies on traditional 2D layout and bounding box conditions. Other baselines include MagicDrive, Panacea, and MagicDriveDiT.

6. Results & Analysis

Core Results

The quantitative results demonstrate CoGen's superiority.

(Manual Transcription of Table 1) Table 1. Quantitative comparison on video generation quality with other methods. Our method achieves the best FVD score.

• Analysis: CoGen achieves an FVD of 68.43, which is a massive improvement over the next best method, DiVE (94.60). This indicates significantly better temporal consistency and motion realism. The FID of 10.15 is also the best, showing superior per-frame visual quality.

(Manual Transcription of Table 2) Table 2. Comparison with baselines for video generation controllability. Results are calculated with first 16 frames of videos.

• Analysis: For downstream tasks, CoGen again leads. It achieves an mIoU of 37.80 and an mAP of 27.88, outperforming the strong DiVE baseline by 5.1% and 13.6% respectively. This proves that the generated videos are not just visually pleasing but also geometrically accurate enough to be useful for training perception models.

  该图像是自动驾驶视频生成的示意图，展示了三帧（50、100、150）中不同视角下的连续场景，体现了方法在多视角和时间上的3D一致性与空间适应性。图中用虚线和红框标注了车辆运动轨迹和目标对象，突出几何保真度。

• Qualitative Analysis (Figure 4): This figure showcases a generated video sequence. The scene is complex ("Rainy day"), and the video maintains remarkable consistency across frames (sampled 4.2 seconds apart) and across different camera views. The yellow arrows highlight how a car's movement is tracked coherently over time and space, demonstrating the model's strong temporal and 3D consistency. The red box shows a large vehicle appearing correctly in the final frame.

  该图像是对比示意图，展示了MagicDrive与本文方法在自动驾驶视频生成中车辆细节和空间一致性的差异。图中包括真实场景、条件输入以及两种生成方法的视觉效果，凸显本文方法在保持多视角几何一致性方面的优势。

• Qualitative Analysis (Figure 5): This is a direct comparison with MagicDrive. The top row shows MagicDrive's output, where parked cars appear distorted and "melt" into each other. The middle row shows CoGen's output, where the cars are much more distinct, well-formed, and geometrically consistent, closely matching the real scene in the bottom row. This visually confirms the benefits of using fine-grained 3D conditions.

Ablation Studies

The paper conducts several ablation studies to validate the contribution of each new component.

(Manual Transcription of Table 3) Table 3. Ablation results on 16-frame video generation.

• Studies on 3D Semantics Conditions:

  • Row (3) vs. (4): Using both Semantic Map (Sem) and Depth Map (Dep) (FVD 69.54) is better than using just the Semantic Map (FVD 72.67), showing that explicit depth information is crucial.
  • Row (1) vs. (5): Using Semantic Map and Depth Map ($S+D$) as conditions (FVD 68.43) seems to work better than using MPI and Coordinate Map (FVD 71.10), at least in this setup.
  • Row (5) vs. (6): Using ground-truth (GT) 3D semantics (FVD 68.43) gives better results than using generated (GEN) 3D semantics (FVD 70.85), which is expected. However, the performance with generated semantics is still very strong, demonstrating the viability of the two-stage generation pipeline.

• Studies on Consistency Adapter:

  • Row (4) vs. (5): Adding the Consistency Adapter to the $S+D$ setup improves the FVD from 69.54 to 68.43, a noticeable gain in temporal consistency.

    该图像是一个折线图，展示了不同模型配置在不同时长视频（帧数）上的FVD评测结果，FVD数值越低表示生成质量越好。图中展示基础模型（Baseline）及依次加入S、D、A模块后的性能提升，标注了数值和相对降低的百分比，体现了各模块对视频生成质量的提升效果。

  • Figure 7 further reinforces this. The blue line (Baseline w/ S,D & A) is consistently lower (better) than the green line (Baseline w/ S & D) across different video lengths (8, 16, 28, 40 frames), proving the adapter's effectiveness in maintaining long-range temporal coherence.

• Studies on Mask Loss:

  该图像是对比示意图，展示了在无占用掩码损失和有占用掩码损失情况下生成驾驶视频帧的效果差异。下方带有占用掩码损失的结果在细节和一致性上表现更佳，红色框突出显示关键区域的改进。

  • Figure 6 shows a qualitative comparison. The top row (without mask loss) shows blurry pedestrians with poor detail. The bottom row (with mask loss) shows much sharper and more distinct pedestrians, even in the distance. This highlights the effectiveness of the foreground-aware loss in improving the fidelity of critical objects.

• Partial 3D Semantics Usage:

  (Manual Transcription of Table 4) Table 4. Comparative results for training and inference under varying 3D Semantics and traditional layout/bounding box (BEV) guidance ratios.

  Train (3D-SEM/BEV)		Infer (3D-SEM/BEV)		FVD↓	FID↓
  0%	100%	100%	0%	120.65	20.45
  0%	100%	0%	100%	103.70	19.46
  100%	0%	100%	0%	68.43	10.15
  50%	50%	100%	0%	73.82	11.74
  50%	50%	0%	100%	73.80	11.86

  • This experiment explores a more practical scenario where high-quality 3D semantic labels are not always available. A model was trained on a 50/50 mix of 3D semantic conditions and traditional BEV conditions. The results (last two rows) show that this hybrid model performs remarkably well, with FVD scores around 73.8. This is only a slight degradation compared to the model trained purely on 3D semantics (FVD 68.43) and is far better than the model trained purely on BEV (FVD 103.70). This demonstrates the model's flexibility and robustness.

7. Conclusion & Reflections

• Conclusion Summary: The paper introduces CoGen, a novel framework for generating 3D-consistent and photorealistic driving videos. By pioneering a "3D-first" approach that uses fine-grained 3D semantic projections as guidance, it overcomes the limitations of prior methods based on coarse 2D layouts. The inclusion of a Consistency Adapter and a Foreground-Mask Loss further boosts temporal coherence and detail fidelity. The state-of-the-art results on the nuScenes benchmark, both in terms of visual quality (FVD) and downstream task utility (mAP, mIoU), validate CoGen as a powerful and reliable solution for synthesizing high-quality data for autonomous driving.

• Limitations & Future Work:

  • Two-Stage Pipeline: The current approach requires a two-stage process: first generating the 3D semantics, then generating the video. This can be complex and computationally intensive. An end-to-end model that generates both jointly could be a future direction.
  • Dependency on 3D Semantics Quality: The quality of the final video is highly dependent on the quality of the generated 3D semantics. Errors or artifacts in the 3D generation stage can propagate to the final video.
  • Computational Cost: Diffusion models, especially for high-resolution video, are computationally expensive to train and run. While LDMs help, the overall framework remains demanding.

• Personal Insights & Critique:

  • Novelty and Impact: The core idea of using generated 3D semantics as a rich conditioning signal is highly innovative and effectively addresses the fundamental problem of 3D consistency in multi-view generation. This is a significant step forward from simply using 2D maps and represents a paradigm shift in controllable video synthesis for autonomous driving. The impact could be substantial, enabling the creation of safer and more robust perception and planning models by training them on a nearly infinite supply of diverse and geometrically accurate synthetic data.
  • Transferability: The concept of generating a high-fidelity intermediate representation (like 3D semantics) before the final synthesis could be applied to other domains beyond autonomous driving, such as robotics (simulating object interactions) or virtual reality (creating dynamic environments).
  • Open Questions:
    • How well does the model generalize to out-of-distribution scenarios or different geographical locations not present in the training data (e.g., snowy conditions, different road architectures)?
    • Can the Consistency Adapter be made even more efficient or powerful? Its current design is effective but relatively simple.
    • The paper focuses on visual generation. Integrating other modalities, like LiDAR point clouds or radar, into this 3D-centric framework would be a compelling next step toward creating a complete "digital twin" of the real world.