hongfz16/AvatarCLIP

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1100 stars · 99 forks observed · 2026-08-28

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

Official PyTorch implementation of AvatarCLIP, a SIGGRAPH 2022 research framework that generates and animates 3D human avatars from natural-language descriptions of body shape, appearance, and motion. It uses CLIP to supervise neural human geometry, texture generation via volume rendering, and CLIP-guided motion synthesis with motion VAE priors.

Use cases

• generate a 3D avatar from a text description
• animate a 3D character with a text-described motion
• create stylized 3D humans without modeling software
• text-driven 3D human shape and texture generation
• research baseline for zero-shot avatar generation
• produce animated avatar clips for demos

When to choose

• you want to generate 3D avatars purely from natural language
• you need a research reference implementation of CLIP-guided 3D human generation
• you have a GPU environment and want to reproduce SIGGRAPH 2022 results

When to avoid

• you need production-ready avatar tooling with a polished UI
• you lack a CUDA GPU or the SMPL body model assets
• you need a permissively licensed codebase for commercial use

Facets

library · maturity maintenance

machine-learning deep-learning graphics simulation nlp computer-vision graphics artificial-intelligence python 3d-avatars clip text-to-3d neural-rendering motion-generation research-code siggraph natural-language-processing gpu linux

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markdown · JSON · MCP: product_card(name="hongfz16/AvatarCLIP")