# dreamgaussian/dreamgaussian

[ICLR 2024 Oral] Generative Gaussian Splatting for Efficient 3D Content Creation

Repository: https://github.com/dreamgaussian/dreamgaussian
Canonical: https://ross.abutalabs.com/products/dreamgaussian
Homepage: https://dreamgaussian.github.io/
Language: Python
License: MIT
License Family: permissive
Topics: image-to-3d, text-to-3d
Last push: 2024-01-02T04:12:09+00:00

## Health v2 (maintenance only)
Score: 18/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 76
- inputs: {"age_days": 1071, "days_push": 974, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4352, forks 399 (observed 2026-08-28T04:08:46.346235+00:00)

## What it is
DreamGaussian is the official PyTorch implementation of an ICLR 2024 Oral paper for efficient 3D content creation using generative Gaussian Splatting. It generates textured 3D meshes from a single image or text prompt in about two minutes, roughly 10x faster than prior score-distillation methods.

## Use cases
- generate a 3D model from a single photo
- create 3D assets from text prompts
- convert an image into a textured mesh for games
- rapid 3D prototyping from concept art
- research on 3D generative models and Gaussian splatting

## When to choose
- you need fast image-to-3D or text-to-3D generation on a single consumer GPU
- you want exportable textured meshes rather than just radiance fields
- you are reproducing or building on published 3D generation research

## When to avoid
- you need production-grade, artist-ready 3D assets with fine topology
- you lack a CUDA-capable GPU
- you need a polished end-user application rather than research code

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, graphics, stable-diffusion
- domain: artificial-intelligence, computer-vision, graphics, deep-learning
- platform: windows, python
- tags: 3d-generation, gaussian-splatting, text-to-3d, image-to-3d, mesh-generation, research-code, iclr-2024, linux, gpu

## Member repositories
- dreamgaussian/dreamgaussian (main) score 18

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:46.346235+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 2026-08-29T18:21:31.648111+00:00, confidence not recorded.
  - readme: https://github.com/dreamgaussian/dreamgaussian (fetched 2026-08-28T04:08:46.346235+00:00, sha 526cd34778a8)
  - homepage: https://dreamgaussian.github.io/ (fetched 2026-08-29T09:10:24.877092+00:00, sha 256abad0307b)
- Data as of 2026-08-30T08:39:29.467469+00:00.