DD-NeRF: Double-Diffusion Neural Radiance Field as a Generalizable Implicit Body Representation

• URL: http://arxiv.org/abs/2112.12390v1
• Date: Thu, 23 Dec 2021 07:30:22 GMT
• Title: DD-NeRF: Double-Diffusion Neural Radiance Field as a Generalizable Implicit Body Representation
• Authors: Guangming Yao, Hongzhi Wu, Yi Yuan, Kun Zhou
• Abstract summary: We present DD-NeRF, a novel generalizable implicit field for representing human body geometry and appearance from arbitrary input views. Experiments on various datasets show that the proposed approach outperforms previous works in both geometry reconstruction and novel view synthesis quality.
• Score: 17.29933848598768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present DD-NeRF, a novel generalizable implicit field for representing human body geometry and appearance from arbitrary input views. The core contribution is a double diffusion mechanism, which leverages the sparse convolutional neural network to build two volumes that represent a human body at different levels: a coarse body volume takes advantage of unclothed deformable mesh to provide the large-scale geometric guidance, and a detail feature volume learns the intricate geometry from local image features. We also employ a transformer network to aggregate image features and raw pixels across views, for computing the final high-fidelity radiance field. Experiments on various datasets show that the proposed approach outperforms previous works in both geometry reconstruction and novel view synthesis quality.

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