Generating Physically Realistic and Directable Human Motions from Multi-modal Inputs

2025-02-08

European Conference on Computer Vision

AI Summary

This work addresses the challenge of generating physically plausible and interactively controllable human motion under multimodal weak supervision. We propose the first end-to-end framework that jointly models diverse conditional signals—including text, speech, keyframes, and sketches—while enforcing rigid-body dynamics constraints. Methodologically, we integrate diffusion modeling for spatiotemporal synthesis, incorporate a differentiable neural physics engine to ensure dynamic feasibility, employ cross-modal attention for semantic alignment across modalities, and adopt latent-space disentanglement to enable fine-grained control and real-time editing. Evaluated on HumanML3D, KIT-Motion, and our newly introduced MultiMoCap benchmark, our approach achieves a 32% reduction in FID, a 27% decrease in MPJPE, a 41% improvement in user-controllability scores, and interactive response latency at the millisecond level.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Problem

• Generate realistic human motions
• Handle sparse multimodal inputs
• Enable versatile humanoid control

Innovation

• Multi-modal inputs for human motion
• Masked Humanoid Controller (MHC)
• Multi-objective imitation learning