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D-NeRF is a method for synthesizing novel views, at an arbitrary point in time, of dynamic scenes with complex non-rigid geometries. We optimize an underlying deformable volumetric function from a sparse set of input monocular views without the need of ground-truth geometry nor multi-view images.
The main features of albertpumarola/d-nerf are: Deformable Scenes.
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… 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". google/nerfies — This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies. googleinterns/ibrnet — PyTorch implementation of paper "IBRNet: Learning Multi-View Image-Based Rendering", CVPR 2021. hustvl/tineuvox — TiNeuVox: Fast Dynamic Radiance Fields with Time-Aware Neural Voxels (SIGGRAPH Asia 2022).
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
Code for "HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields".
This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies.
Dynamic Neural Radiance Fields for Monocular 4D Facial Avater Reconstruction