Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images

• URL: http://arxiv.org/abs/2111.04237v1
• Date: Mon, 8 Nov 2021 02:16:48 GMT
• Title: Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images
• Authors: Jianfei Guo, Zhiyuan Yang, Xi Lin, Qingfu Zhang
• Abstract summary: We present neural radiance fields (NeRF) with templates, dubbed template-NeRF, for modeling appearance and geometry. We generate dense shape correspondences simultaneously among objects of the same category from only multi-view posed images. The learned dense correspondences can be readily used for various image-based tasks such as keypoint detection, part segmentation, and texture transfer.
• Score: 4.662583832063716
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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