Papers › Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation

Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation

24 Mar 2022CVPR 2022 1arXiv:2203.13161archive 2025-07-28

Xian Liu, Qianyi Wu, Hang Zhou, Yinghao Xu, Rui Qian, Xinyi Lin, Xiaowei Zhou, Wayne Wu, Bo Dai, Bolei Zhou

Generating speech-consistent body and gesture movements is a long-standing problem in virtual avatar creation. Previous studies often synthesize pose movement in a holistic manner, where poses of all joints are generated simultaneously. Such a straightforward pipeline fails to generate fine-grained co-speech gestures. One observation is that the hierarchical semantics in speech and the hierarchical structures of human gestures can be naturally described into multiple granularities and associated together. To fully utilize the rich connections between speech audio and human gestures, we propose a novel framework named Hierarchical Audio-to-Gesture (HA2G) for co-speech gesture generation. In HA2G, a Hierarchical Audio Learner extracts audio representations across semantic granularities. A Hierarchical Pose Inferer subsequently renders the entire human pose gradually in a hierarchical manner. To enhance the quality of synthesized gestures, we develop a contrastive learning strategy based on audio-text alignment for better audio representations. Extensive experiments and human evaluation demonstrate that the proposed method renders realistic co-speech gestures and outperforms previous methods in a clear margin. Project page: https://alvinliu0.github.io/projects/HA2G

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Code

alvinliu0/HA2G officialmentioned on GitHubpytorchGPL-3.0 report

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Tasks

Contrastive LearningGesture Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Gesture Generation BEAT2 HA2G FGD 1.232 #12 of 14 Archive leaderboard report
Gesture Generation TED Gesture Dataset HA2G FGD 3.072 #3 of 6 Archive leaderboard report

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Methods

Contrastive Learning

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