elliottwu/unsup3d

Health v2 · maintenance only

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1189 stars · 191 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of the CVPR 2020 (Oral, Best Paper Award) research paper 'Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild' from Oxford's Visual Geometry Group. It learns 3D shape, pose, albedo, and lighting of deformable object categories (e.g., faces, cats) from raw single-view images without any 3D ground truth or keypoint supervision.

Use cases

• reconstruct 3D objects from single images without 3D ground truth
• learn 3D face shape from unannotated photos
• run unsupervised 3D reconstruction research experiments
• reproduce the Unsup3D CVPR 2020 paper results
• estimate depth, albedo, and lighting from a single image
• train a 3D-aware model on cat or human face datasets

When to choose

• you need a research baseline for unsupervised single-view 3D reconstruction
• you want to reproduce or extend the CVPR 2020 Unsup3D paper
• you have single-view image collections of symmetric deformable objects like faces
• you need a reference implementation with a pretrained demo

When to avoid

• you need production-ready, actively maintained 3D reconstruction software
• you lack a GPU, since training and testing require GPU-only neural rendering
• you need modern PyTorch versions, as the code targets PyTorch 1.2.0 and CUDA 9.2
• your objects are not roughly symmetric or deformable

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning python 3d-reconstruction unsupervised-learning pytorch cvpr-2020 single-view-3d deformable-objects research-code neural-rendering linux gpu

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