GPC: Large-Scale Generative Pretraining for Transferable Motor Control
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 character animation. In this work, we introduce Generative Pretrained Controllers (GPC), which leverage tokenization and next-token modeling to create general-purpose, reusable generative controllers from large-scale motion datasets. Our framework utilizes end-to-end reinforcement learning to jointly optimize a "motion vocabulary", modeled via Finite Scalar Quantization (FSQ), along with a corresponding control policy that can map the discrete codes to physics-based controls. After the "codebook" has been learned, the u...
| Authors | Yi Shi, Yifeng Jiang, Chen Tessler et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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