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.
Abstract (translated)
URL
https://arxiv.org/abs/2112.12390
