mlsanityData to 14 Oct 2026

Audio2Rig: Artist-oriented deep learning tool for facial animation

arXiv 2405.20412 ↗First posted 30 May 2024cs.GR cs.LGcited by 0

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Abstract (arXiv)

Creating realistic or stylized facial and lip sync animation is a tedious task. It requires lot of time and skills to sync the lips with audio and convey the right emotion to the character's face. To allow animators to spend more time on the artistic and creative part of the animation, we present Audio2Rig: a new deep learning based tool leveraging previously animated sequences of a show, to generate facial and lip sync rig animation from an audio file. Based in Maya, it learns from any production rig without any adjustment and generates high quality and stylized animations which mimic the style of the show. Audio2Rig fits in the animator workflow: since it generates keys on the rig controllers, the animation can be easily retaken. The method is based on 3 neural network modules which can learn an arbitrary number of controllers. Hence, different configurations can be created for specific parts of the face (such as the tongue, lips or eyes). With Audio2Rig, animators can also pick different emotions and adjust their intensities to experiment or customize the output, and have high level controls on the keyframes setting. Our method shows excellent results, generating fine animation details while respecting the show style. Finally, as the training relies on the studio data and is done internally, it ensures data privacy and prevents from copyright infringement.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 4 references that are in the map. First posted on arXiv 30 May 2024.

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References · 1

  1. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304

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