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• W3214389227 abstract "We present neural radiance fields (NeRF) with templates, dubbed Template-NeRF, for modeling appearance and geometry and generating dense shape correspondences simultaneously among objects of the same category from only multi-view posed images, without the need of either 3D supervision or ground-truth correspondence knowledge. The learned dense correspondences can be readily used for various image-based tasks such as keypoint detection, part segmentation, and texture transfer that previously require specific model designs. Our method can also accommodate annotation transfer in a one or few-shot manner, given only one or a few instances of the category. Using periodic activation and feature-wise linear modulation (FiLM) conditioning, we introduce deep implicit templates on 3D data into the 3D-aware image synthesis pipeline NeRF. By representing object instances within the same category as shape and appearance variation of a shared NeRF template, our proposed method can achieve dense shape correspondences reasoning on images for a wide range of object classes. We demonstrate the results and applications on both synthetic and real-world data with competitive results compared with other methods based on 3D information." @default.
• W3214389227 created "2021-11-22" @default.
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• W3214389227 date "2021-11-07" @default.
• W3214389227 modified "2023-09-25" @default.
• W3214389227 title "Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images" @default.
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• W3214389227 doi "https://doi.org/10.48550/arxiv.2111.04237" @default.
• W3214389227 hasPublicationYear "2021" @default.
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