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- W4387293468 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." @default.
- W4387293468 created "2023-10-03" @default.
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- W4387293468 date "2023-09-28" @default.
- W4387293468 modified "2023-10-04" @default.
- W4387293468 title "Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis" @default.
- W4387293468 doi "https://doi.org/10.48550/arxiv.2309.16859" @default.
- W4387293468 hasPublicationYear "2023" @default.
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