TL_Control: Trajectory and Language Control for Human Motion Synthesis

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.

Please download the SMPL+H body model from https://mano.is.tue.mpg.de/ and place it in the directory:
./body_models/

Run the following command to install the necessary dependencies:

pip install -r requirements.txt

Note:
For demo visualization support, you might also need to install additional libraries through the following command:

sudo apt-get install libgl1-mesa-glx libglu1-mesa freeglut3-dev libglib2.0-dev libgl1-mesa-dri libosmesa6-dev

• Masked-Trajectory Transformer Weights: Download from Google Drive and place the file in:
  ./save_weights/

• VQ-VAE Pre-trained Weights: Download from Google Drive and place the file in:
  ./save_weights_vq/

Execute the following command to run the demo:

python -m demo.demo

The core operations of TLControl are located in ./demo/demo.py.
We recommend starting with this file to understand how the system works.

We would like to express our gratitude to the following projects, which provided valuable support for this project:

• Humor
• T2M-GPT
• HumanML3D
• Motion Diffusion Model