SemTalk: Holistic Co-speech Motion Generation with Frame-level Semantic Emphasis

• URL: http://arxiv.org/abs/2412.16563v2
• Date: Wed, 15 Jan 2025 13:34:12 GMT
• Title: SemTalk: Holistic Co-speech Motion Generation with Frame-level Semantic Emphasis
• Authors: Xiangyue Zhang, Jianfang Li, Jiaxu Zhang, Ziqiang Dang, Jianqiang Ren, Liefeng Bo, Zhigang Tu,
• Abstract summary: A good co-speech motion generation cannot be achieved without a careful integration of common rhythmic motion and rare yet essential semantic motion. We propose SemTalk for holistic co-speech motion generation with frame-level semantic emphasis.
• Score: 19.764460501254607
• License:
• Abstract: A good co-speech motion generation cannot be achieved without a careful integration of common rhythmic motion and rare yet essential semantic motion. In this work, we propose SemTalk for holistic co-speech motion generation with frame-level semantic emphasis. Our key insight is to separately learn general motions and sparse motions, and then adaptively fuse them. In particular, rhythmic consistency learning is explored to establish rhythm-related base motion, ensuring a coherent foundation that synchronizes gestures with the speech rhythm. Subsequently, textit{semantic emphasis learning is designed to generate semantic-aware sparse motion, focusing on frame-level semantic cues. Finally, to integrate sparse motion into the base motion and generate semantic-emphasized co-speech gestures, we further leverage a learned semantic score for adaptive synthesis. Qualitative and quantitative comparisons on two public datasets demonstrate that our method outperforms the state-of-the-art, delivering high-quality co-speech motion with enhanced semantic richness over a stable base motion.

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