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• W2944736082 abstract "We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu can produce high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image." @default.
• W2944736082 created "2019-05-16" @default.
• W2944736082 creator A5007091110 @default.
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• W2944736082 date "2019-05-13" @default.
• W2944736082 modified "2023-09-26" @default.
• W2944736082 title "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization" @default.
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• W2944736082 doi "https://doi.org/10.48550/arxiv.1905.05172" @default.
• W2944736082 hasPublicationYear "2019" @default.
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