4 papers · 1 filter
GPC: Large-Scale Generative Pretraining for Transferable Motor Control
Yi Shi, Yifeng Jiang, Chen Tessler +1
Developing controllers capable of completing a wide range of tasks in a natural and life-like manner is a key challenge in enabling practical applications of physics-based characte…
Kimodo: Scaling Controllable Human Motion Generation
Davis Rempe, Mathis Petrovich, Ye Yuan +21
High-quality human motion data is becoming increasingly important for applications in robotics, simulation, and entertainment. Recent generative models offer a potential data sourc…
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control
Guy Tevet, Sigal Raab, Setareh Cohan +5
Motion diffusion models and Reinforcement Learning (RL) based control for physics-based simulations have complementary strengths for human motion generation. The former is capable…
Flexible Motion In-betweening with Diffusion Models
Setareh Cohan, Guy Tevet, Daniele Reda +2
Motion in-betweening, a fundamental task in character animation, consists of generating motion sequences that plausibly interpolate user-provided keyframe constraints. It has long…