Name
Title | ||
|---|---|---|
DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering |
Abstract | ||
|---|---|---|
We introduce DoubleField, a novel framework combining the merits of both surface field and radiance field for high-fidelity human reconstruction and rendering. Within DoubleField, the surface field and radiance field are associated together by a shared feature embedding and a surface-guided sampling strategy. Moreover, a view-to-view transformer is introduced to fuse multi-view features and learn view-dependent features directly from high-resolution inputs. With the modeling power of DoubleField and the view-to-view transformer, our method significantly improves the reconstruction quality of both geometry and appearance, while supporting direct inference, scene-specific high-resolution finetuning, and fast rendering. The efficacy of DoubleField is validated by the quantitative evaluations on several datasets and the qualitative results in a real-world sparse multi-view system, showing its superior capability for high-quality human model reconstruction and photo-realistic free-viewpoint human rendering. Data and source code will be made public for the research purpose. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.01541 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
3D from multi-view and sensors, 3D from single images, Image and video synthesis and generation | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruizhi Shao | 1 | 0 | 0.34 |
| Hongwen Zhang | 2 | 0 | 1.01 |
| He Zhang | 3 | 0 | 0.34 |
| Mingjia Chen | 4 | 0 | 0.34 |
| Yan-Pei Cao | 5 | 0 | 0.34 |
| Tao Yu | 6 | 8 | 5.87 |
| Yebin Liu | 7 | 688 | 49.05 |