Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis

• URL: http://arxiv.org/abs/2309.16859v1
• Date: Thu, 28 Sep 2023 21:21:44 GMT
• Title: Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis
• Authors: Marcel C. B\"uhler (1 and 2), Kripasindhu Sarkar (2), Tanmay Shah (2), Gengyan Li (1 and 2), Daoye Wang (2), Leonhard Helminger (2), Sergio Orts-Escolano (2), Dmitry Lagun (2), Otmar Hilliges (1), Thabo Beeler (2), Abhimitra Meka (2) ((1) ETH Zurich, (2) Google)
• Abstract summary: NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin. We propose a novel human face prior that enables the synthesis of ultra high-resolution novel views of subjects that are not part of the prior's training distribution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin. These methods typically require a large number of multi-view input images, making the process hardware intensive and cumbersome, limiting applicability to unconstrained settings. We propose a novel volumetric human face prior that enables the synthesis of ultra high-resolution novel views of subjects that are not part of the prior's training distribution. This prior model consists of an identity-conditioned NeRF, trained on a dataset of low-resolution multi-view images of diverse humans with known camera calibration. A simple sparse landmark-based 3D alignment of the training dataset allows our model to learn a smooth latent space of geometry and appearance despite a limited number of training identities. A high-quality volumetric representation of a novel subject can be obtained by model fitting to 2 or 3 camera views of arbitrary resolution. Importantly, our method requires as few as two views of casually captured images as input at inference time.

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