UDE: A Unified Driving Engine for Human Motion Generation

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 16 518. In this paper, we propose “UDE”, the first unified driving engine that enables generating human motion sequences from natural language or audio sequences (see Fig. 1). 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 33, 2) a modality-agnostic transformer encoder 34 that learns to map modality-aware driving signals to a joint space, and 3) a unified token transformer (GPT-like24)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 sequence swith high diversity. Weevaluate our method on HumanML3D 8 and AIST+ 19 benchmarks, and the experiment results demonstrate our method achieves state-of-the-art performance. Project website: https://zixiangzhou916.github.io/UDE/