# cwchenwang/awesome-3d-diffusion

A collection of papers on diffusion models for 3D generation.

Repository: https://github.com/cwchenwang/awesome-3d-diffusion
Canonical: https://ross.abutalabs.com/products/awesome-3d-diffusion
License: MIT
License Family: permissive
Last push: 2026-01-16T22:18:01+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 62, release rhythm 35, longevity 78
- inputs: {"age_days": 1103, "days_push": 229, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1254, forks 60 (observed 2026-08-28T04:04:08.672083+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, graphics
- domain: artificial-intelligence, deep-learning, graphics, awesome-lists
- platform: cross-platform
- tags: awesome-list, diffusion-models, 3d-generation, survey, papers, text-to-3d

## Member repositories
- cwchenwang/awesome-3d-diffusion (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:08.672083+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:07:23.760030+00:00, confidence not recorded.
  - readme: https://github.com/cwchenwang/awesome-3d-diffusion (fetched 2026-08-28T04:04:08.672083+00:00, sha 2fc2cf6a98f1)
- Data as of 2026-08-30T08:39:29.467469+00:00.