@inproceedings{
mao2024mdtag,
title={{MDT}-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation},
author={Xiaofeng Mao and Zhengkai Jiang and Qilin Wang and Chencan Fu and Jiangning Zhang and Jiafu Wu and Yabiao Wang and Chengjie Wang and Wei Li and Mingmin Chi},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=xJK53lGJP2}
}
Recent advancements in the field of Diffusion Transformers have substantially improved the generation of high-quality 2D images, 3D videos, and 3D shapes. However, the effectiveness of the Transformer architecture in the domain of co-speech gesture generation remains relatively unexplored, as prior methodologies have predominantly employed the Convolutional Neural Network (CNNs) or simple a few transformer layers. In an attempt to bridge this research gap, we introduce a novel Masked Diffusion Transformer for co-speech gesture generation, referred to as MDT-A2G, which directly implements the denoising process on gesture sequences. To enhance the contextual reasoning capability of temporally aligned speech-driven gestures, we incorporate a novel Masked Diffusion Transformer. This model employs a mask modeling scheme specifically designed to strengthen temporal relation learning among sequence gestures, thereby expediting the learning process and leading to coherent and realistic motions. Apart from audio, Our MDT-A2G model also integrates multi-modal information, encompassing text, emotion, and identity. Furthermore, we propose an efficient inference strategy that diminishes the denoising computation by leveraging previously calculated results, thereby achieving a speedup with negligible performance degradation. Experimental results demonstrate that MDT-A2G excels in gesture generation, boasting a learning speed that is over 6$\times$ faster than traditional diffusion transformers and an inference speed that is 5.7$\times$ than the standard diffusion model.
MDT-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation
Xiaofeng Mao Zhengkai Jiang Qilin Wang Chencan Fu Jiangning Zhang Jiafu Wu Yabiao Wang Chengjie Wang Wei Li Mingmin Chi