Democratizing High-Fidelity Co-Speech Gesture Video Generation

Xu Yang; Shaoli Huang; Shenbo Xie; Xuelin Chen; Yifei Liu; Changxing Ding

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arXiv · 2507.06812

Democratizing High-Fidelity Co-Speech Gesture Video Generation

Xu Yang·Shaoli Huang·Shenbo Xie·Xuelin Chen·Yifei Liu·Changxing Ding

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Abstract

Co-speech gesture video generation aims to synthesize realistic, audio-aligned videos of speakers, complete with synchronized facial expressions and body gestures. This task presents challenges due to the significant one-to-many mapping between audio and visual content, further complicated by the scarcity of large-scale public datasets and high computational demands. We propose a lightweight framework that utilizes 2D full-body skeletons as an efficient auxiliary condition to bridge audio signals with visual outputs. Our approach introduces a diffusion model conditioned on fine-grained audio segments and a skeleton extracted from the speaker's reference image, predicting skeletal motions through skeleton-audio feature fusion to ensure strict audio coordination and body shape consistency. The generated skeletons are then fed into an off-the-shelf human video generation model with the speaker's reference image to synthesize high-fidelity videos. To democratize research, we present CSG-405-the first public dataset with 405 hours of high-resolution videos across 71 speech types, annotated with 2D skeletons and diverse speaker demographics. Experiments show that our method exceeds state-of-the-art approaches in visual quality and synchronization while generalizing across speakers and contexts. Code, models, and CSG-405 are publicly released at https://mpi-lab.github.io/Democratizing-CSG/

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Xu Yang, Shaoli Huang, Shenbo Xie, Xuelin Chen, Yifei Liu, Changxing Ding. 2025-07-09. Democratizing High-Fidelity Co-Speech Gesture Video Generation. https://arxiv.org/abs/2507.06812

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