KSDiff: Keyframe-Augmented Speech-Aware Dual-Path Diffusion for Facial Animation

• URL: http://arxiv.org/abs/2509.20128v1
• Date: Wed, 24 Sep 2025 13:54:52 GMT
• Title: KSDiff: Keyframe-Augmented Speech-Aware Dual-Path Diffusion for Facial Animation
• Authors: Tianle Lyu, Junchuan Zhao, Ye Wang,
• Abstract summary: KSDiff is a Keyframe-Augmented Speech-Aware Dual-Path Diffusion framework.<n>It disentangles expression-related and head-pose-related features, while an autoregressive Keyframe Establishment Learning module predicts the most salient motion frames.<n>Experiments on HDTF and VoxCeleb demonstrate that KSDiff state-of-the-art performance, with improvements in both lip synchronization accuracy and head-pose naturalness.
• Score: 4.952724424448834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-driven facial animation has made significant progress in multimedia applications, with diffusion models showing strong potential for talking-face synthesis. However, most existing works treat speech features as a monolithic representation and fail to capture their fine-grained roles in driving different facial motions, while also overlooking the importance of modeling keyframes with intense dynamics. To address these limitations, we propose KSDiff, a Keyframe-Augmented Speech-Aware Dual-Path Diffusion framework. Specifically, the raw audio and transcript are processed by a Dual-Path Speech Encoder (DPSE) to disentangle expression-related and head-pose-related features, while an autoregressive Keyframe Establishment Learning (KEL) module predicts the most salient motion frames. These components are integrated into a Dual-path Motion generator to synthesize coherent and realistic facial motions. Extensive experiments on HDTF and VoxCeleb demonstrate that KSDiff achieves state-of-the-art performance, with improvements in both lip synchronization accuracy and head-pose naturalness. Our results highlight the effectiveness of combining speech disentanglement with keyframe-aware diffusion for talking-head generation.

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