TesserAct: Learning 4D Embodied World Models (2504.20995v1)

Abstract: This paper presents an effective approach for learning novel 4D embodied world models, which predict the dynamic evolution of 3D scenes over time in response to an embodied agent's actions, providing both spatial and temporal consistency. We propose to learn a 4D world model by training on RGB-DN (RGB, Depth, and Normal) videos. This not only surpasses traditional 2D models by incorporating detailed shape, configuration, and temporal changes into their predictions, but also allows us to effectively learn accurate inverse dynamic models for an embodied agent. Specifically, we first extend existing robotic manipulation video datasets with depth and normal information leveraging off-the-shelf models. Next, we fine-tune a video generation model on this annotated dataset, which jointly predicts RGB-DN (RGB, Depth, and Normal) for each frame. We then present an algorithm to directly convert generated RGB, Depth, and Normal videos into a high-quality 4D scene of the world. Our method ensures temporal and spatial coherence in 4D scene predictions from embodied scenarios, enables novel view synthesis for embodied environments, and facilitates policy learning that significantly outperforms those derived from prior video-based world models.

An Analysis of TesserAct: Learning 4D Embodied World Models

The paper presents "TesserAct: Learning 4D Embodied World Models," which introduces a novel approach for predicting the dynamics of 3D scenes over time in response to interactions by an embodied intelligent agent. This paradigm shift is achieved through integrating 4D embodied world models that include RGB, depth, and normal information within video predictions. The paper's methodology surpasses the previous limitations associated with 2D pixel space models, providing enriched spatial and temporal consistency essential for robotic manipulation tasks.

Core Contributions

The authors contribute significantly to embodied world modeling by:

1. Development and Collection of 4D Data:
   • The researchers compiled a comprehensive dataset combining synthetic and real-world videos annotated with depth and normal information. For real-world video datasets that lack 4D annotations, they leveraged state-of-the-art video depth estimators and normal map estimators to enhance diversity and alignment with real-world scenarios.
2. Advanced Model Architecture:
   • TesserAct employs a latent video diffusion model fine-tuned with 4D embodied video datasets. By utilizing RGB-DN (RGB, Depth, and Normal) data, the architecture efficiently predicts dynamic 4D scenes, using intermediate representations conducive to robust policy learning.
3. Optimized Scene Reconstruction Algorithm:
   • The paper introduces an efficient algorithm that capitalizes on generated RGB-DN videos to construct high-quality 4D scenes while maintaining temporal and spatial coherence across time frames. The use of optical flow combined with depth and normal map predictions ensures accurate reconstruction of dynamic components within scenes.
4. Impact on Downstream Tasks:
   • The embodied action planning benefits from predicted 4D point clouds, facilitating robust policy synthesis and data-driven simulations. This advancement significantly enhances the performance and accuracy of robotic tasks, such as those demonstrated in RLBench.

Strong Numerical Performance

The evaluation indicates that TesserAct achieves superior results in reconstructing 4D scenes compared to current video diffusion models. Performance metrics such as FVD, SSIM, and Chamfer distance underscore the method's ability to generate high-quality dynamic scenes, maintain detail, and enhance the accuracy of robotic task execution.

Implications and Future Prospects

The theoretical implications of this research are profound, as TesserAct effectively bridges the gap between 2D models and the real-world complexity of 3D embodied systems. Practically, this breakthrough enables enhanced robotic autonomy in executing tasks involving intricate object interactions, which previously required extensive 3D information.

In terms of future developments, the grounding of real-world policy training within this 4D framework opens avenues for more sophisticated simulated environments where planning can be done offline. The potential next step includes refining the model for multi-view and complete 4D world reconstructions, further expanding its application scope. Moreover, integrating these principles with AI-driven adaptive learning strategies could lead to more dynamic and responsive embodied agents capable of navigating increasingly realistic environments with greater competence.

In conclusion, TesserAct marks a notable progression in embodied AI research by introducing a viable solution to model 4D dynamics accurately. The approach sets a foundation for further advancements that can extend simulated environments into practical applications across diverse domains in robotics and autonomous systems.