# dunbar12138/pix2pix3D

pix2pix3D: Generating 3D Objects from 2D User Inputs

Repository: https://github.com/dunbar12138/pix2pix3D
Canonical: https://ross.abutalabs.com/products/pix2pix3d
Homepage: http://www.cs.cmu.edu/~pix2pix3D/
Language: Python
License: MIT
License Family: permissive
Topics: 3d-gan, eg3d, pix2pix3d
Last push: 2023-09-13T18:55:33+00:00

## Health v2 (maintenance only)
Score: 31/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 93
- inputs: {"age_days": 1304, "days_push": 1085, "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 1716, forks 149 (observed 2026-08-28T04:05:26.385611+00:00)

## What it is
pix2pix3D is the official PyTorch implementation of a CVPR 2023 paper on 3D-aware conditional image synthesis. It generates 3D objects (neural fields) from 2D label maps such as segmentation or edge maps, with an interactive 3D editing demo.

## Use cases
- generate 3D objects from 2D segmentation maps
- synthesize photorealistic images from edge maps with controllable viewpoints
- edit 3D content interactively from any viewpoint
- train a 3D-aware conditional GAN on monocular image and label map pairs
- render semantic meshes from generated neural fields
- convert face segmentation maps into 3D-consistent face images

## When to choose
- you need viewpoint-consistent 3D generation conditioned on 2D label maps
- you want an interactive tool for 3D-aware image editing
- you are reproducing or building on the CVPR 2023 3D-aware conditional synthesis paper
- you need pre-trained models for seg2face, seg2cat, or edge2car tasks

## When to avoid
- you need a production-ready application with a polished UI
- you lack a CUDA-capable GPU, since training and inference are compute-intensive
- you need general-purpose 3D modeling rather than label-map-conditioned generation
- you need actively maintained code with frequent updates and support

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, computer-vision, graphics
- domain: computer-vision, graphics, machine-learning, artificial-intelligence
- platform: python
- tags: 3d-gan, neural-radiance-fields, conditional-generative-model, pix2pix, eg3d, cvpr-2023, pytorch, research-code, 3d-editing, neural-fields, linux, gpu

## Member repositories
- dunbar12138/pix2pix3D (main) score 31

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:26.385611+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 2026-08-30T03:34:15.295518+00:00, confidence not recorded.
  - readme: https://github.com/dunbar12138/pix2pix3D (fetched 2026-08-28T04:05:26.385611+00:00, sha 9845d22336cd)
  - homepage: http://www.cs.cmu.edu/~pix2pix3D/ (fetched 2026-08-29T11:10:17.738077+00:00, sha 53c54b00c7a1)
- Data as of 2026-08-30T08:39:29.467469+00:00.