@techreport{Qian_report2020,
TITLE = {Parametric Hand Texture Model for {3D} Hand Reconstruction and Personalization},
AUTHOR = {Qian, Neng and Wang, Jiayi and Mueller, Franziska and Bernard, Florian and Golyanik, Vladislav and Theobalt, Christian},
LANGUAGE = {eng},
ISSN = {0946-011X},
NUMBER = {MPI-I-2020-4-001},
INSTITUTION = {Max-Planck-Institut f{\"u}r Informatik},
ADDRESS = {Saarbr{\"u}cken},
YEAR = {2020},
ABSTRACT = {3D hand reconstruction from image data is a widely-studied problem in com-<br>puter vision and graphics, and has a particularly high relevance for virtual<br>and augmented reality. Although several 3D hand reconstruction approaches<br>leverage hand models as a strong prior to resolve ambiguities and achieve a<br>more robust reconstruction, most existing models account only for the hand<br>shape and poses and do not model the texture. To {fi}ll this gap, in this work<br>we present the {fi}rst parametric texture model of human hands. Our model<br>spans several dimensions of hand appearance variability (e.g., related to gen-<br>der, ethnicity, or age) and only requires a commodity camera for data acqui-<br>sition. Experimentally, we demonstrate that our appearance model can be<br>used to tackle a range of challenging problems such as 3D hand reconstruc-<br>tion from a single monocular image. Furthermore, our appearance model<br>can be used to de{fi}ne a neural rendering layer that enables training with a<br>self-supervised photometric loss. We make our model publicly available.},
TYPE = {Research Report},
}