Reproduce “TLControl: Trajectory and Language Control for Human Motion Synthesis” on OpenResearch
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About this paper
Weilin Wan, Zhiyang Dou, Taku Komura, Wenping Wang, Dinesh Jayaraman, Lingjie Liu
TLControl introduces a method for human motion synthesis that integrates precise multi-joint trajectory control with high-level language semantics. The system achieves superior accuracy in aligning synthesized motion with user-specified trajectories and demonstrates significantly improved computational efficiency compared to current diffusion-based approaches.
Key insights
- Learning a part-based discrete latent motion space provides compact, structured representations that enable precise and flexible control over individual body parts.
- Combining a transformer for initial coarse motion prediction with efficient test-time latent space optimization effectively integrates high-level language semantics and low-level trajectory control.
- The use of masking strategies during transformer training allows the model to robustly handle partial or incomplete trajectory inputs from users.
Summary by alphaXiv.
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