This AI paper introduces PEVA: a whole-body conditioned diffusion model for predicting self-centered videos from human movement

Understand the connection between body movement and visual perception

Research on human visual perception through a self-centered perspective is crucial to developing intelligent systems that can understand and interact with the environment. This field emphasizes how the movement of the human body (from movement to arm manipulation) is viewed from a first-person perspective. Understanding this relationship is essential to enable machines and robots to plan and act in human visually expected plans, especially in the reality that visibility is affected by dynamics of body movement.

Challenges of Modeling on Physical Based

A major obstacle in this field stems from the challenges of how teaching systems affect perception. Actions such as turning or bending change visible in subtle and often delayed ways. Capturing this feature requires not only simple prediction of the next step in the video, but also involves linking physical motion to the result changes in visual input. Without the ability to explain and simulate these changes, the agents embody efforts to plan or interact effectively in a dynamic environment.

Limitations of previous models and the need for physical grounding

So far, tools designed to predict videos of human action have been limited in scope. Models often use low-dimensional inputs, such as velocity or head orientation, and ignore the complexity of whole-body movement. These simplified approaches ignore the fine-grained control and coordination required to accurately simulate human behavior. Even in video generation models, body movement is often seen as output rather than a driving force for prediction. The lack of physical basis limits the usefulness of these models for real-world planning.

Introduction to PEVA: Self-centered Videos in Action Prediction

Researchers from UC Berkeley, Meta Expo and New York University have launched a new framework called PEVA to overcome these limitations. The model predicts future self-centered video frameworks based on structured whole-body motion data derived from 3D body posture trajectory. PEVA aims to demonstrate how overall movement affects what a person sees, thus rooting the connection between action and perception. The researchers used a conditional diffusion transformer to learn this mapping and trained it using Nymeria, a large dataset that includes real-world central videos synchronized with full-body motion capture.

Structured action representation and model architecture

The basis of PEVA is its ability to represent actions in a highly structured way. Each action input is a 48-dimensional vector that includes root translation and joint rotation across 15 upper body joints in 3D space. This vector is normalized and transformed into a local coordinate frame centered around the pelvis to eliminate any positional deviation. By leveraging this comprehensive representation of body dynamics, the model captures the continuity and nuance of real movement. PEVA is designed as a cyclo-diffusion model that uses a video encoder to convert frames into latent state representations and predicts subsequent frames based on previous states and body movements. To support long-term video generation, the system introduces random time keys during the training process, allowing it to be learned from direct and delayed motor consequences.

Performance evaluation and results

In terms of performance, PEVA is tested several types of tests, both of which test short-term and long-term video prediction capabilities. The model is able to generate visually consistent and semantically accurate video frames over an extended time. For short-term predictions, evaluated at 2-second intervals, it has a lower LPIPS score compared to the benchmark, while DreamsIM has a higher consistency, indicating excellent perceived quality. The system also breaks down human motion into atomic behaviors, such as arm movement and body rotation, to evaluate fine-grained controls. In addition, the model was tested on an extended rollout of up to 16 seconds, successfully simulating the delayed results while maintaining sequence coherence. These experiments confirm that the combination of whole-body control leads to a significant improvement in video realism and controllability.

Conclusion: Embodied intelligence towards the root of physics

This study highlights significant advances in predicting future self-centered videos by rooting their models in physical motion. The problem of connecting whole-body movements to visual outcomes is solved by a technically robust approach that uses structured posture representations and diffusion-based learning. The solutions introduced by the team provide promising directions for the embodied AI systems and require accurate, physical vision.

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