Deform2NeRF: Non-Rigid Deformation and 2D-3D Feature Fusion with Cross-Attention for Dynamic Human Reconstruction

Xiaolong Xie, Xusheng Guo,Wei Li,Jie Liu,Jianfeng Xu


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Reconstructing dynamic human body models from multi-view videos poses a substantial challenge in the field of 3D computer vision. Currently, the Animatable NeRF method addresses this challenge by mapping observed points from the viewing space to a canonical space. However, this mapping introduces positional shifts in predicted points, resulting in artifacts, particularly in intricate areas. In this paper, we propose an innovative approach called Deform2NeRF that incorporates non-rigid deformation correction and image feature fusion modules into the Animatable NeRF framework to enhance the reconstruction of animatable human models. Firstly, we introduce a non-rigid deformation field network to address the issue of point position shift effectively. This network adeptly corrects positional discrepancies caused by non-rigid deformations. Secondly, we introduce a 2D-3D feature fusion learning module with cross-attention and integrate it with the NeRF network to mitigate artifacts in specific detailed regions. Our experimental results demonstrate that our method significantly improves the PSNR index by approximately 5% compared to representative methods in the field. This remarkable advancement underscores the profound importance of our approach in the domains of new view synthesis and digital human reconstruction.
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