4 papers
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…
Robo-Saber: Generating and Simulating Virtual Reality Players
Nam Hee Kim, Jingjing May Liu, Jaakko Lehtinen +3
We present the first motion generation system for playtesting virtual reality (VR) games. Our player model generates VR headset and handheld controller movements from in-game objec…
MaskedManipulator: Versatile Whole-Body Manipulation
Chen Tessler, Yifeng Jiang, Erwin Coumans +3
We tackle the challenges of synthesizing versatile, physically simulated human motions for full-body object manipulation. Unlike prior methods that are focused on detailed motion t…