GitHub - yukangcao/JIFFContribute to yukangcao/JIFF development by creating an account on GitHub.github.com  JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human ReconstructionThis paper addresses the problem of single view 3D human reconstruction. Recent implicit function based methods have shown impressive results, but they fail to recover fine face details i..

DoubleField Project PageWe 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 feawww.liuyebin.com  DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction ..

https://www.matthewtancik.com/nerf NeRF: Neural Radiance FieldsA method for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views.www.matthewtancik.com AbstractWe present a method that achieves SOTA results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene ..

3D Gaussian Splatting for Real-Time Radiance Field Rendering[Müller 2022] Müller, T., Evans, A., Schied, C. and Keller, A., 2022. Instant neural graphics primitives with a multiresolution hash encoding [Hedman 2018] Hedman, P., Philip, J., Price, T., Frahm, J.M., Drettakis, G. and Brostow, G., 2018. Deep blendingrepo-sam.inria.frAbstractRadiance Field method : 여러 장의 이미지나 비디오로 novel-view synthesi..

https://arxiv.org/abs/2103.14030 Swin Transformer: Hierarchical Vision Transformer using Shifted WindowsThis paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such asarxiv.org이번 포스팅은 2021 ICCV에 accept된 Sw..

Abstract복잡한 pose를 취하고 있는 사람이나 옷이나 헤어스타일 등을 리얼하게 복구하는 것은 보이지 않는 영역을 예측하는 것 뿐만 아니라 중요한 task들 중 하나로 여겨져 왔다. 하지만 이전의 모델들은 2D image를 3D로 변환하고 texture를 예측하는 것에 있어서 prior guidance가 충분하지 않다는 점이 문제가 되어왔다. 따라서 본 논문에서는 SIFU [Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction]이라는 모델을 제안해서 이를 해결하고자 했다.SIFU는 transformer의 cross-mechanism을 사용하였고, SMPL-X를 이용해서 2D feature들을..