Efficient integration of motion understanding and deformation control in feed-forward 3D vision architectures

Develop techniques to efficiently integrate motion understanding and deformation control into feed-forward 3D vision architectures for dynamic 3D content generation and animation, ensuring accurate, robust, and fast prediction without reliance on iterative optimization or diffusion loops.

Background

Feed-forward models have recently achieved strong performance in static 3D reconstruction and related tasks by producing high-quality assets in a single forward pass, avoiding iterative optimization or diffusion. However, extending this paradigm to dynamic 3D content and animation involves additional complexity, such as understanding motion and controlling geometry deformation over time.

The paper situates its contribution within this context and notes that, despite progress, the broader challenge of marrying motion understanding and deformation control with feed-forward 3D architectures remains unresolved in the community.