EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling

Published in

Computer Vision and Pattern Recognition(2023)

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TL;DR

Experiments demonstrate that EMAGE generates holistic gestures with state-of-the-art performance and is flexible in accepting predefined spatial-temporal gesture inputs, generating complete, audio-synchronized results.

Abstract

We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements. To achieve this, we first introduce BEAT2 (BEAT-SMPLX-FLAME), a new mesh-level holistic co-speech dataset. BEAT2 combines a MoShed SMPL-X body with FLAME head parameters and further refines the modeling of head, neck, and finger movements, offering a community-standardized, high-quality 3D motion captured dataset. EMAGE leverages masked body gesture priors during training to boost inference performance. It involves a Masked Audio Gesture Transformer, facilitating joint training on audio-to-gesture generation and masked gesture reconstruction to effectively encode audio and body gesture hints. Encoded body hints from masked gestures are then separately employed to generate facial and body movements. Moreover, EMAGE adaptively merges speech features from the audio's rhythm and content and utilizes four compositional VQ-VAEs to enhance the results' fidelity and diversity. Experiments demonstrate that EMAGE generates holistic gestures with state-of-the-art performance and is flexible in accepting predefined spatial-temporal gesture inputs, generating complete, audio-synchronized results. Our code and dataset are available. 1

Authors

Haiyang Liu

2 papers

You Zhou

2 papers

Zihao Zhu

1 papers

References81 items

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EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling

Published in

Computer Vision and Pattern Recognition(2023)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Haiyang Liu

2 papers

You Zhou

2 papers

Zihao Zhu

1 papers

References81 items

Emotional Speech-Driven 3D Body Animation via Disentangled Latent Diffusion

Weakly-Supervised Emotion Transition Learning for Diverse 3D Co-Speech Gesture Generation

LivelySpeaker: Towards Semantic-Aware Co-Speech Gesture Generation

Bodyformer: Semantics-guided 3D Body Gesture Synthesis with Transformer

BlendFace: Re-designing Identity Encoders for Face-Swapping

Giorgio Becherini

1 papers

Yichen Peng

1 papers

Mingyang Su

1 papers

Naoya Iwamoto

1 papers

Michael J. Black

1 papers

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Field of Study

Computer Science

Journal Information

Name

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Page

4818-4829

Venue Information

Name

Computer Vision and Pattern Recognition

Type

conference

URL

https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147

Alternate Names

CVPR
Comput Vis Pattern Recognit