Generalizable Object-aware Human Motion Synthesis (Talk)

Data-driven virtual 3D character animation has recently witnessed remarkable progress. The realism of virtual characters is a core contributing factor to the quality of computer animations and user experience in immersive applications like games, movies, and VR/AR. However, existing automatic approaches for 3D virtual character motion synthesis supporting scene interactions do not generalize well to new objects outside training distributions, even when trained on extensive motion capture datasets with diverse objects and annotated interactions. In this talk, I will present ROAM, an alternative framework that generalizes to unseen objects of the same category without relying on a large dataset of human-object animations. In addition, I will share some preliminary findings from an ongoing project on hand motion interaction with articulated objects.