Supasorn Suwajanakorn | Total Moving Face Reconstruction ECCV 2014 @SupasornSuwajanakorn | Uploaded 10 years ago | Updated 3 hours ago
We present an approach that takes a single video of a person's face and reconstructs a high detail 3D shape for each video frame. We target videos taken under uncontrolled and uncalibrated imaging conditions, such as youtube videos of celebrities. ECCV 2014
http://grail.cs.washington.edu/projects/totalmoving/
We present an approach that takes a single video of a person's face and reconstructs a high detail 3D shape for each video frame. We target videos taken under uncontrolled and uncalibrated imaging conditions, such as youtube videos of celebrities. ECCV 2014
http://grail.cs.washington.edu/projects/totalmoving/
![[CVPR 2021] NeX: Real-time View Synthesis with Neural Basis Expansion](https://i.ytimg.com/vi/HyfkF7Z-ddA/hqdefault.jpg "[CVPR 2021] NeX: Real-time View Synthesis with Neural Basis Expansion
This is a supplementary video for
NeX: Real-time View Synthesis with Neural Basis Expansion
by Suttisak Wizadwongsa*, Pakkapon Phongthawee*, Jiraphon Yenphraphai*, Supasorn Suwajanakorn. (* first co-authors)
https://nex-mpi.github.io/
Abstract
We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce NeXt-level view-dependent effects in real time. Unlike traditional MPI that uses a set of simple RGBα planes, our technique models view-dependent effects by instead parameterizing each pixel as a linear combination of basis functions learned from a neural network. Moreover, we propose a hybrid implicit-explicit modeling strategy that improves upon fine detail and produces state-of-the-art results. Our method is evaluated on benchmark forward-facing datasets as well as our newly-introduced dataset designed to test the limit of view-dependent modeling with significantly more challenging effects such as rainbow reflections on a CD. Our method achieves the best overall scores across all major metrics on these datasets with more than 1000× faster rendering time than the state of the art.")
