MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations

• URL: http://arxiv.org/abs/2310.10198v3
• Date: Tue, 19 Dec 2023 16:44:46 GMT
• Title: MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations
• Authors: Heyuan Yao, Zhenhua Song, Yuyang Zhou, Tenglong Ao, Baoquan Chen, Libin Liu
• Abstract summary: MoConVQ is a novel unified framework for physics-based motion control leveraging scalable discrete representations. Our approach effectively learns motion embeddings from a large, unstructured dataset spanning tens of hours of motion examples.
• Score: 25.630268570049708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present MoConVQ, a novel unified framework for physics-based motion control leveraging scalable discrete representations. Building upon vector quantized variational autoencoders (VQ-VAE) and model-based reinforcement learning, our approach effectively learns motion embeddings from a large, unstructured dataset spanning tens of hours of motion examples. The resultant motion representation not only captures diverse motion skills but also offers a robust and intuitive interface for various applications. We demonstrate the versatility of MoConVQ through several applications: universal tracking control from various motion sources, interactive character control with latent motion representations using supervised learning, physics-based motion generation from natural language descriptions using the GPT framework, and, most interestingly, seamless integration with large language models (LLMs) with in-context learning to tackle complex and abstract tasks.

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