Exploiting Temporal Audio-Visual Correlation Embedding for Audio-Driven One-Shot Talking Head Animation

• URL: http://arxiv.org/abs/2504.05746v1
• Date: Tue, 08 Apr 2025 07:23:28 GMT
• Title: Exploiting Temporal Audio-Visual Correlation Embedding for Audio-Driven One-Shot Talking Head Animation
• Authors: Zhihua Xu, Tianshui Chen, Zhijing Yang, Siyuan Peng, Keze Wang, Liang Lin,
• Abstract summary: Inherently, the temporal relationship of adjacent audio clips is highly correlated with that of the corresponding adjacent video frames.<n>We learn audio-visual correlations and integrate the correlations to help enhance feature representation and regularize final generation.
• Score: 62.218932509432314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paramount challenge in audio-driven One-shot Talking Head Animation (ADOS-THA) lies in capturing subtle imperceptible changes between adjacent video frames. Inherently, the temporal relationship of adjacent audio clips is highly correlated with that of the corresponding adjacent video frames, offering supplementary information that can be pivotal for guiding and supervising talking head animations. In this work, we propose to learn audio-visual correlations and integrate the correlations to help enhance feature representation and regularize final generation by a novel Temporal Audio-Visual Correlation Embedding (TAVCE) framework. Specifically, it first learns an audio-visual temporal correlation metric, ensuring the temporal audio relationships of adjacent clips are aligned with the temporal visual relationships of corresponding adjacent video frames. Since the temporal audio relationship contains aligned information about the visual frame, we first integrate it to guide learning more representative features via a simple yet effective channel attention mechanism. During training, we also use the alignment correlations as an additional objective to supervise generating visual frames. We conduct extensive experiments on several publicly available benchmarks (i.e., HDTF, LRW, VoxCeleb1, and VoxCeleb2) to demonstrate its superiority over existing leading algorithms.

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