Controllable human motion synthesis is essential for applications in AR/VR,
gaming, movies, and embodied AI. Existing methods often focus solely on either
language or full trajectory control, lacking precision in synthesizing motions
aligned with user-specified trajectories, especially for multi-joint control.
To address these issues, we present TLControl, a new method for realistic human
motion synthesis, incorporating both low-level trajectory and high-level
language semantics controls. Specifically, we first train a VQ-VAE to learn a
compact latent motion space organized by body parts. We then propose a Masked
Trajectories Transformer to make coarse initial predictions of full
trajectories of joints based on the learned latent motion space, with
user-specified partial trajectories and text descriptions as conditioning.
Finally, we introduce an efficient test-time optimization to refine these
coarse predictions for accurate trajectory control. Experiments demonstrate
that TLControl outperforms the state-of-the-art in trajectory accuracy and time
efficiency, making it practical for interactive and high-quality animation
generation.

TLControl 是一种新的方法，结合了低级轨迹和高级语义控制，用于合成真实的人类动作，在轨迹准确性和时间效率方面胜过现有技术，可实用于交互式和高质量的动画生成。