Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders

Generating conversational gestures from speech audio is challenging due to the inherent one-to-many mapping be-tween audio and body motions. Conventional CNNs/RNNs assume one-to-one mapping, and thus tend to predict the average of all possible target motions, resulting in plain/boring motions during...

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Bibliographic Details
Published in2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 11273 - 11282
Main Authors Li, Jing, Kang, Di, Pei, Wenjie, Zhe, Xuefei, Zhang, Ying, He, Zhenyu, Bao, Linchao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2021
Subjects
Action and behavior recognition
Bicycles
Codes
Computer vision
Correlation
Gestures and body pose
Speech coding
Three-dimensional displays
Training
Vision + other modalities
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Summary:Generating conversational gestures from speech audio is challenging due to the inherent one-to-many mapping be-tween audio and body motions. Conventional CNNs/RNNs assume one-to-one mapping, and thus tend to predict the average of all possible target motions, resulting in plain/boring motions during inference. In order to over-come this problem, we propose a novel conditional variational autoencoder (VAE) that explicitly models one-to-many audio-to-motion mapping by splitting the cross-modal latent code into shared code and motion-specific code. The shared code mainly models the strong correlation between audio and motion (such as the synchronized audio and motion beats), while the motion-specific code captures diverse motion information independent of the audio. However, splitting the latent code into two parts poses training difficulties for the VAE model. A mapping network facilitating random sampling along with other techniques including relaxed motion loss, bicycle constraint, and diversity loss are designed to better train the VAE. Experiments on both 3D and 2D motion datasets verify that our method generates more realistic and diverse motions than state-of-the-art methods, quantitatively and qualitatively. Finally, we demonstrate that our method can be readily used to generate motion sequences with user-specified motion clips on the timeline. Code and more results are at https://jingli513.github.io/audio2gestures.
ISSN:2380-7504
DOI:10.1109/ICCV48922.2021.01110