cwchenwang/awesome-3d-diffusion resource
A collection of papers on diffusion models for 3D generation. observed · 2026-08-28
Health v2 · maintenance only
56/100
- Activity 62
- Release rhythm 35
- Longevity 78
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1103
- days_rel: n/a
- days_push: 229
- n_releases_24m: 0
Adoption not part of the score
1254 stars · 60 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A curated awesome-list of research papers on diffusion models for 3D generation, accompanied by a survey paper. It organizes papers by technique categories such as text-to-3D, image-to-3D, and diffusion in 3D space.
Use cases
- find papers on diffusion models for 3D generation
- research text-to-3D methods like DreamFusion
- survey the state of the art in 3D content creation with AI
- find papers on multi-view diffusion and 3D editing
- get started researching generative 3D models
When to choose
- you need a curated reading list of 3D diffusion research
- you are writing a literature review on generative 3D content creation
When to avoid
- you need runnable code or a library rather than a paper list
- you want a complete exhaustive index rather than a curated selection
Facets
learning-resource · maturity active
machine-learning deep-learning graphics artificial-intelligence deep-learning graphics awesome-lists cross-platform awesome-list diffusion-models 3d-generation survey papers text-to-3d
1 source
- readme: https://github.com/cwchenwang/awesome-3d-diffusion · fetched 2026-08-28 · 2fc2cf6a98f1
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| cwchenwang/awesome-3d-diffusion | main | 56 |
For agents
markdown · JSON · MCP: product_card(name="cwchenwang/awesome-3d-diffusion")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem