Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders
This addresses the problem of generating engaging and varied body motions for virtual agents or animations from audio, though it is incremental as it builds on existing VAE methods with specific modifications.
The paper tackles the challenge of generating diverse conversational gestures from speech audio, which has a one-to-many mapping, by proposing a conditional variational autoencoder that splits the latent code into shared and motion-specific parts, resulting in more realistic and diverse motions than state-of-the-art methods, as verified quantitatively and qualitatively on 3D and 2D datasets.
Generating conversational gestures from speech audio is challenging due to the inherent one-to-many mapping between 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 overcome 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.