# justimyhxu/awesome-3D-generation

A curated list of awesome 3d generation papers

Repository: https://github.com/justimyhxu/awesome-3D-generation
Canonical: https://ross.abutalabs.com/products/awesome-3d-generation
License Family: other
Last push: 2023-03-09T14:55:46+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1643, "days_push": 1273, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1198, forks 61 (observed 2026-08-28T04:03:57.625190+00:00)

## What it is
A curated awesome-list of research papers on 3D generation, covering 3D shape generation and 3D-aware image generation, accompanied by an arXiv survey. It organizes works by representation type such as point cloud, voxel, mesh, and neural field.

## Use cases
- find papers on 3d shape generation
- survey of 3d-aware image generation research
- learn about generative models for point clouds and meshes
- track the evolution of deep generative 3d models
- find code implementations for 3d generation papers
- research neural field and NeRF generative methods

## When to choose
- you need a structured reading list of 3D generation research with links to code
- you are surveying generative models across different 3D representations
- you want an academic overview backed by a published survey paper

## When to avoid
- you need runnable software or a library rather than a paper list
- you need actively maintained coverage of the newest 2024+ papers
- you are looking for 3D reconstruction rather than generation

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, graphics
- domain: deep-learning, computer-vision, graphics, tutorials
- platform: cross-platform
- tags: awesome-list, 3d-generation, generative-models, papers, survey, neural-fields, point-cloud

## Member repositories
- justimyhxu/awesome-3D-generation (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:57.625190+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 2026-08-30T06:21:10.481925+00:00, confidence not recorded.
  - readme: https://github.com/justimyhxu/awesome-3D-generation (fetched 2026-08-28T04:03:57.625190+00:00, sha 5eb8972763c2)
- Data as of 2026-08-30T08:39:29.467469+00:00.