Artist-Friendly Relightable and Animatable Neural Heads

• URL: http://arxiv.org/abs/2312.03420v1
• Date: Wed, 6 Dec 2023 11:06:46 GMT
• Title: Artist-Friendly Relightable and Animatable Neural Heads
• Authors: Yingyan Xu, Prashanth Chandran, Sebastian Weiss, Markus Gross, Gaspard Zoss, Derek Bradley
• Abstract summary: A common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields. Recent variants also surpassed the usual drawback of baked-in illumination in neural representations, showing that static neural avatars can be relit in any environment. Our method builds on a proven dynamic avatar approach based on a mixture of volumetric primitives, combined with a recently-proposed lightweight hardware setup for relightable neural fields.
• Score: 15.803111500220888
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields. The original neural radiance field (NeRF) allowed for impressive novel view synthesis of static heads when trained on a set of multi-view images, and follow up methods showed that these neural representations can be extended to dynamic avatars. Recently, new variants also surpassed the usual drawback of baked-in illumination in neural representations, showing that static neural avatars can be relit in any environment. In this work we simultaneously tackle both the motion and illumination problem, proposing a new method for relightable and animatable neural heads. Our method builds on a proven dynamic avatar approach based on a mixture of volumetric primitives, combined with a recently-proposed lightweight hardware setup for relightable neural fields, and includes a novel architecture that allows relighting dynamic neural avatars performing unseen expressions in any environment, even with nearfield illumination and viewpoints.

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