# princeton-vl/infinigen

Infinite Photorealistic Worlds using Procedural Generation

Repository: https://github.com/princeton-vl/infinigen
Canonical: https://ross.abutalabs.com/products/infinigen
Homepage: https://infinigen.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Last push: 2026-08-26T14:23:49+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 93, longevity 83
- inputs: {"age_days": 1173, "days_push": 7, "days_rel": 49, "gap_med": 9, "n_releases_24m": 16}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7222, forks 607 (observed 2026-08-28T04:09:57.574126+00:00)

## What it is
Infinigen is a procedural generator of infinite photorealistic 3D worlds and scenes, built on Blender by the Princeton Vision & Learning Lab. It produces diverse high-quality 3D training data (RGB, depth, segmentation, surface normals) for computer vision research, including indoor scenes, nature scenes, and articulated simulation assets.

## Use cases
- generate synthetic training data for computer vision models
- create photorealistic 3D indoor scenes procedurally
- generate infinite nature landscapes with ground truth annotations
- export articulated assets to simulators
- render depth maps and instance segmentation from generated scenes
- create diverse 3D datasets for machine learning research

## When to choose
- you need large-scale synthetic 3D training data with perfect ground truth labels
- you want procedurally generated photorealistic indoor or outdoor scenes
- you need simulation-ready assets for robotics or RL environments
- you are doing computer vision research requiring diverse rendered scenes

## When to avoid
- you need a lightweight real-time 3D engine for games
- you want hand-crafted art assets rather than procedural generation
- you lack GPU/compute resources for Blender-based rendering
- you need non-photorealistic stylized content

## Facets
- artifact type: library
- maturity: active
- function: simulation, graphics, image-processing, machine-learning, data-generation
- domain: computer-vision, graphics, simulation, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: procedural-generation, blender, synthetic-data, 3d-scenes, photorealistic, training-data, cvpr, linux

## Member repositories
- princeton-vl/infinigen (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:57.574126+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 2026-08-29T17:39:48.685522+00:00, confidence not recorded.
  - readme: https://github.com/princeton-vl/infinigen (fetched 2026-08-28T04:09:57.574126+00:00, sha e3072467dd6b)
  - homepage: https://infinigen.org (fetched 2026-08-29T08:34:42.957091+00:00, sha 2ab35cdf826c)
  - site_page: https://infinigen.org/docs-contributing/begin (fetched 2026-08-29T08:34:42.966990+00:00, sha 80755aa0f078)
- Data as of 2026-08-30T08:39:29.467469+00:00.