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PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes"
The main features of zhengqili/neural-scene-flow-fields are: Deformable Scenes, Video Synthesis.
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… chungyiweng/humannerf — HumanNeRF turns a monocular video of moving people into a 360 free-viewpoint video. fanegg/uv-volumes — [CVPR 2023] UV Volumes for Real-time Rendering of Editable Free-view Human Performance. gafniguy/4d-facial-avatars — Dynamic Neural Radiance Fields for Monocular 4D Facial Avater Reconstruction. google/hypernerf — Code for "HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields". algohunt/streamrf — Official implementation of our NeurIPS paper "Streaming Radiance Fields for 3D Video Synthesis".
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
HumanNeRF turns a monocular video of moving people into a 360 free-viewpoint video.
CVPR 2023 UV Volumes for Real-time Rendering of Editable Free-view Human Performance
Official implementation of our NeurIPS paper "Streaming Radiance Fields for 3D Video Synthesis"