RECORD
Audio2Rig: Artist-oriented deep learning tool for facial animation
Graphics · Submitted 30 May 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our method shows excellent results, generating fine animation details while respecting the show style.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Creating realistic or stylized facial and lip sync animation is a tedious task. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s1 | It requires lot of time and skills to sync the lips with audio and convey the right emotion to the character's face. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s2 | 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. | contribution 1.00 | 0.80 | 0.21 | asserted 0.99 |
| s3 | 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. | method 0.92 | 0.64 | 0.08 | asserted 1.00 |
| s4 | Audio2Rig fits in the animator workflow: since it generates keys on the rig controllers, the animation can be easily retaken. | method 0.42 | 0.60 | 0.07 | asserted 0.97 |
| s5 | The method is based on 3 neural network modules which can learn an arbitrary number of controllers. | method 1.00 | 0.78 | 0.05 | asserted 1.00 |
| s6 | Hence, different configurations can be created for specific parts of the face (such as the tongue, lips or eyes). | method 0.94 | 0.60 | 0.03 | asserted 0.85 |
| s7 | 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. | contribution 0.68 | 0.65 | 0.07 | asserted 0.78 |
| s8 | Our method shows excellent results, generating fine animation details while respecting the show style. | result 0.99 | 0.93 | 0.63 | asserted 1.00 |
| s9 | Finally, as the training relies on the studio data and is done internally, it ensures data privacy and prevents from copyright infringement. | interpretation 0.50 | 0.65 | 0.10 | asserted 1.00 |
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The paper
Original abstract
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.
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