Audio2AB: Audio-driven collaborative generation of virtual character animation

Abstract

Background: Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success. However, few methods exist for generating full-body animations, and the portability of virtual character gestures and facial animations has not received sufficient attention. Methods: Therefore, we propose a deep-learning-based audio-to-animation-and-blendshape (Audio2AB) network that generates gesture animations andARK it’s 52 facial expression parameter blendshape weights based on audio, audio-corresponding text, emotion labels, and semantic relevance labels to generate parametric data for full- body animations. This parameterization method can be used to drive full-body animations of virtual characters and improve their portability. In the experiment, we first downsampled the gesture and facial data to achieve the same temporal resolution for the input, output, and facial data. The Audio2AB network then encoded the audio, audio- corresponding text, emotion labels, and semantic relevance labels, and then fused the text, emotion labels, and semantic relevance labels into the audio to obtain better audio features. Finally, we established links between the body, gestures, and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function. Results: By using audio, audio-corresponding text, and emotional and semantic relevance labels as input, the trained Audio2AB network could generate gesture animation data containing blendshape weights. Therefore, different 3D virtual character animations could be created through parameterization. Conclusions: The experimental results showed that the proposed method could generate significant gestures and facial animations.

Keywords

• Audio-driven
• Virtual character
• Full-body animation
• Audio2AB
• Blendshape
• GAN-GF