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This project is a computer vision pipeline and volumetric rendering system used to transform photos and videos into high-fidelity 3D models. It implements a deformable neural radiance field framework that optimizes deformation fields to represent non-rigid moving subjects in three dimensions. The system utilizes volumetric deformation fields to map 3D coordinates from a static canonical space to a deformed state. This allows for the reconstruction of photorealistic scenes and the synthesis of high-fidelity images from camera perspectives not present in the original input data. The framework
ICCV 2021 Our work presents a novel neural rendering approach that can efficiently reconstruct geometric and neural radiance fields for view synthesis.
Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"
PyTorch implementation of paper "IBRNet: Learning Multi-View Image-Based Rendering", CVPR 2021.
The main features of googleinterns/ibrnet are: Deformable Scenes, Generalization Models.
Projects with overlapping indexed features include: nerfies/nerfies.github.io — This project is a computer vision pipeline and volumetric rendering system used to transform photos and videos into… apchenstu/mvsnerf — [ICCV 2021] Our work presents a novel neural rendering approach that can efficiently reconstruct geometric and neural… autonomousvision/graf — Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis". chungyiweng/humannerf — HumanNeRF turns a monocular video of moving people into a 360 free-viewpoint video. gafniguy/4d-facial-avatars — Dynamic Neural Radiance Fields for Monocular 4D Facial Avater Reconstruction. alextrevithick/grf — 🔥 General Radiance Field (ICCV, 2021).
