|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM390662712 |
| 003 |
DE-627 |
| 005 |
20250807232054.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
250806s2025 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2025.3574845
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1523.xml
|
| 035 |
|
|
|a (DE-627)NLM390662712
|
| 035 |
|
|
|a (NLM)40478711
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Guo, Zhiyang
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a HandNeRF++
|b Modeling Animatable Interacting Hands With Neural Radiance Fields
|
| 264 |
|
1 |
|c 2025
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 07.08.2025
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a In this work, we explore the rendering of photo-realistic free-viewpoint hand pose animation. We present HandNeRF, the first NeRF-based framework to reconstruct accurate appearance and geometry for interacting hands. To overcome the texture contamination and shape artifact problems when dealing with complex interacting scenarios, we further introduce HandNeRF++ to achieve better performance. In our advanced framework, a pose-driven deformation field is designed to establish correspondence from diverse poses to a canonical space, where the pose- and shape-disentangled NeRFs are optimized. To enhance the geometry and texture cues in rarely-observed areas for interacting hands, we establish a connection between the interacting hands by proposing the adaptive hand-sharing technique for cross-hand augmentation. Meanwhile, we further leverage the hand poses to generate fine-grained density priors, serving as valuable guidance for occlusion-aware geometry learning. Furthermore, a neural feature distillation method and a neural refiner are proposed to facilitate color optimization and further polish the renderings. With the collaboration of all the modules and strategies, our HandNeRF++ significantly advances the capabilities of NeRF-based 3D reconstruction in the context of interacting hands. Extensive experiments are conducted to validate the merits of the proposed frameworks. We report a series of state-of-the-art results both qualitatively and quantitatively
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Zhou, Wengang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wang, Min
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Li, Li
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Li, Houqiang
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 47(2025), 9 vom: 06. Aug., Seite 8102-8116
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
|
| 773 |
1 |
8 |
|g volume:47
|g year:2025
|g number:9
|g day:06
|g month:08
|g pages:8102-8116
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2025.3574845
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 47
|j 2025
|e 9
|b 06
|c 08
|h 8102-8116
|