💥💥💥 The demo code is now available!!!
✨✨✨ Our paper has been accepted by CVPR2023!
Our shared Unified Driving Engine (UDE) can support both text-driven and audio-driven human motion generation. Left shows an example of a motion sequence driven by a text description while Right shows an example driven by a LA Hiphop music clip
Generating controllable and editable human motion sequences is a key challenge in 3D Avatar generation. It has been labor-intensive to generate and animate human motion for a long time until learning-based approaches have been developed and applied recently. However, these approaches are still task-specific or modality-specific. In this paper, we propose “UDE”, the first unified driving engine that enables generating human motion sequences from natural language or audio sequences. Specifically, UDE consists of the following key components: 1) a motion quantization module based on VQVAE that represents continuous motion sequence as discrete latent code, 2) a modality-agnostic transformer encoder that learns to map modality-aware driving signals to a joint space, and 3) a unified token transformer (GPT-like) network to predict the quantized latent code index in an auto-regressive manner. 4) a diffusion motion decoder that takes as input the motion tokens and decodes them into motion sequences with high diversity. We evaluate our method on HumanML3D and AIST++ benchmarks, and the experiment results demonstrate our method achieves state-of-the-art performance
Our model consists of four key components. First, we train a codebook using VQ-VAE. For the codebook, each code represents a certain pattern of the motion sequence. Second, we introduce a ModalityAgnostic Transformer Encoder (MATE). It takes the input of different modalities and transforms them into sequential embedding in one joint space. The third component is a Unified Token Transformer (UTT). We feed it with sequential embedding obtained by MATE and predict the motion token sequences in an auto-regressive manner. The fourth component is a Diffusion Motion Decoder (DMD). Unlike recent works, which are modality-specific, our DMD is modality-agnostic. Given the motion token sequences, DMD encodes them to semantic-rich embedding and then decodes them to motion sequences in continuous space by the reversed diffusion process.
We show a short demo video on how our model can generate motion sequence with mixed modality of inputs. To watch full demo video, please visit this site: (https://www.youtube.com/embed/CaG1PTvzkxA)
This code was tested on Ubuntu 20.04 LTS and requires:
- Python 3.8
- Conda
- CUDA capable GPU (single GPU works!)
Clone this repo and move it to:
git clone https://github.com/zixiangzhou916/UDE.git
cd UDE
Create a conda enviroment, activate it, and install the dependencies:
pip install -r requirements.txt
The pretrained checkpoints could be downloaded from checkpoint.
tar -xzvf checkpoints.tar.gz
tar -xzvf smpl_models.tar.gz
The unzipped checkpoint files will be organized as:
checkpoints
|--- ude_best.pth
|--- dmd_best.pth
|--- vqvae_best.pth
|--- ViT-B-32.pt
Unzip the files and put move the smpl_models:
mv smpl networks
The smpl models should be organized as:
|networks
|--- smpl
|--- J_regressor_extra.npy
|--- kintree_table.pkl
|--- SMPL_FEMALE.pkl
|--- SMPL_MALE.pkl
|--- SMPL_NEUTRAL.pkl
We provide sample data for quick demo, the sample data are organized as:
|sample_data
|--- t2m
|--- text_descriptions.json
|--- a2m
|--- gHO_sBM_cAll_d21_mHO5_ch01.npy
|---
Run the following command to play with it:
sh demo.sh
Comming soon
Comming soon
@InProceedings{Zhou_2023_CVPR,
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