MoyGcc/vid2avatar

Health v2 · maintenance only

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1360 stars · 110 forks observed · 2026-08-28

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

Vid2Avatar is the official PyTorch implementation of a CVPR 2023 method that reconstructs detailed 3D human avatars from monocular in-the-wild videos. It jointly models the human and background as separate neural fields, self-supervisedly decomposing the scene to produce canonical human geometry and texture without ground-truth supervision or external segmentation.

Use cases

• reconstruct a 3D human avatar from a single monocular video
• separate a moving person from an arbitrary background in 3D
• generate canonical and deformed human mesh sequences from video
• render novel views of a reconstructed clothed human
• research baseline for neural human avatar reconstruction
• visualize SMPL-based human models in 3D

When to choose

• you need a self-supervised 3D human reconstruction pipeline from short monocular clips
• you want a research-grade CVPR 2023 baseline for neural avatar creation
• you have GPU resources and can tolerate 24-48 hour per-sequence training
• you want canonical-space human geometry and texture without labeled data

When to avoid

• you need real-time or fast avatar reconstruction
• you want a polished end-user application rather than research code
• you cannot obtain the SMPL body model or set up a CUDA/conda environment
• your input is multi-view studio footage rather than monocular in-the-wild video

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing graphics simulation computer-vision deep-learning graphics machine-learning python 3d-reconstruction neural-fields human-avatar smpl cvpr-2023 research-code monocular-video scene-decomposition linux gpu

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markdown · JSON · MCP: product_card(name="MoyGcc/vid2avatar")