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URHand: Universal Relightable Hands

Zhaoxi Chen · Gyeongsik Moon · Kaiwen Guo · Chen Cao · Stanislav Pidhorskyi · Tomas Simon · Rohan Joshi · Yuan Dong · Yichen Xu · Bernardo Pires · He Wen · Lucas Evans · Bo Peng · Julia Buffalini · Autumn Trimble · Kevyn McPhail · Melissa Schoeller · Shoou-I Yu · Javier Romero · Michael Zollhoefer · Yaser Sheikh · Ziwei Liu · Shunsuke Saito

Arch 4A-E Poster #65
[ ] [ Project Page ]
Wed 19 Jun 10:30 a.m. PDT — noon PDT
Oral presentation: Orals 1C Humans: Face, body, pose, gesture, movement
Wed 19 Jun 9 a.m. PDT — 10:30 a.m. PDT


Existing photorealistic relightable hand models require extensive identity-specific observations in different views, poses, and illuminations, and face challenges in generalizing to natural illuminations and novel identities. To bridge this gap, we present URHand, the first universal relightable hand model that generalizes across viewpoints, poses, illuminations, and identities. Our model allows few-shot personalization using images captured with a mobile phone, and is ready to be photorealistically rendered under novel illuminations. To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities. The key challenge is scaling the cross-identity training while maintaining personalized fidelity and sharp details without compromising generalization under natural illuminations. To this end, we propose a spatially varying linear lighting model as the neural renderer that takes physics-inspired shading as input feature. By removing non-linear activations and bias, our specifically designed lighting model explicitly keeps the linearity of light transport. This enables single-stage training from light-stage data while generalizing to real-time rendering under arbitrary continuous illuminations across diverse identities. In addition, we introduce the joint learning of a physically based model and our neural relighting model, which further improves fidelity and generalization. Extensive experiments show that our approach achieves superior performance over existing methods in terms of both quality and generalizability. We also demonstrate quick personalization of our universal relightable model from a short phone scan of an unseen identity.

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