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 temporalthis http URLentire 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.
View on arXiv@article{li2025_2504.08344,
title={ EasyGenNet: An Efficient Framework for Audio-Driven Gesture Video Generation Based on Diffusion Model },
author={ Renda Li and Xiaohua Qi and Qiang Ling and Jun Yu and Ziyi Chen and Peng Chang and Mei HanJing Xiao },
journal={arXiv preprint arXiv:2504.08344},
year={ 2025 }
}