EasyGenNet: An Efficient Framework for Audio-Driven Gesture Video Generation Based on Diffusion Model

• URL: http://arxiv.org/abs/2504.08344v1
• Date: Fri, 11 Apr 2025 08:19:18 GMT
• Title: EasyGenNet: An Efficient Framework for Audio-Driven Gesture Video Generation Based on Diffusion Model
• Authors: Renda Li, Xiaohua Qi, Qiang Ling, Jun Yu, Ziyi Chen, Peng Chang, Mei HanJing Xiao,
• Abstract summary: We introduce a new audio-to-video pipeline to synthesize co-speech videos, using 2D human skeleton as the intermediate motion representation.<n>Our experiments show that our method outperforms existing GAN-based and diffusion-based methods.
• Score: 22.286624353800377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-driven cospeech video generation typically involves two stages: speech-to-gesture and gesture-to-video. While significant advances have been made in speech-to-gesture generation, synthesizing natural expressions and gestures remains challenging in gesture-to-video systems. In order to improve the generation effect, previous works adopted complex input and training strategies and required a large amount of data sets for pre-training, which brought inconvenience to practical applications. We propose a simple one-stage training method and a temporal inference method based on a diffusion model to synthesize realistic and continuous gesture videos without the need for additional training of temporal modules.The entire model makes use of existing pre-trained weights, and only a few thousand frames of data are needed for each character at a time to complete fine-tuning. Built upon the video generator, we introduce a new audio-to-video pipeline to synthesize co-speech videos, using 2D human skeleton as the intermediate motion representation. Our experiments show that our method outperforms existing GAN-based and diffusion-based methods.

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