# junshutang/Make-It-3D

[ICCV 2023] Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior

Repository: https://github.com/junshutang/Make-It-3D
Canonical: https://ross.abutalabs.com/products/make-it-3d
Language: Python
License Family: other
Topics: 3d-generation, 3d-vision, computer-vision, deep-learning, diffusion-models, generative-art, nerf
Last push: 2024-07-05T00:01:03+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 89
- inputs: {"age_days": 1259, "days_push": 790, "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 1891, forks 135 (observed 2026-08-28T04:05:49.595782+00:00)

## What it is
Make-It-3D is a research codebase from an ICCV 2023 paper that creates high-fidelity 3D content from a single image using a 2D diffusion model prior. It optimizes a neural radiance field in a coarse stage and then refines it into textured point clouds for realistic 3D reconstruction.

## Use cases
- generate a 3D model from a single photo
- reconstruct 3D geometry and textures from one image
- create 3D assets from text prompts
- edit textures of 3D objects with diffusion priors
- render 360-degree views of an object from one view
- research single-image 3D generation methods

## When to choose
- you need research-grade single-image-to-3D generation with NeRF and diffusion priors
- you want to reproduce or build on an ICCV 2023 3D creation paper
- you have a GPU environment and want to experiment with text-to-3D or image-to-3D pipelines

## When to avoid
- you need a production-ready or user-friendly 3D modeling tool
- you lack a CUDA-capable GPU or cannot set up heavy deep-learning dependencies
- you need a permissively licensed project - the repository has no license, so reuse is legally restricted

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, graphics
- domain: computer-vision, artificial-intelligence, deep-learning, graphics
- platform: python
- tags: 3d-generation, diffusion-models, nerf, single-image-to-3d, iccv-2023, research-code, generative-ai, gpu, linux

## Member repositories
- junshutang/Make-It-3D (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:49.595782+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 2026-08-30T03:13:00.421399+00:00, confidence not recorded.
  - readme: https://github.com/junshutang/Make-It-3D (fetched 2026-08-28T04:05:49.595782+00:00, sha e25f946623b9)
- Data as of 2026-08-30T08:39:29.467469+00:00.