# lukasHoel/text2room

Text2Room generates textured 3D meshes from a given text prompt using 2D text-to-image models (ICCV2023).

Repository: https://github.com/lukasHoel/text2room
Canonical: https://ross.abutalabs.com/products/text2room
Homepage: https://lukashoel.github.io/text-to-room/
Language: Python
License: MIT
License Family: permissive
Topics: 3d-generation, diffusion-models, mesh-generation, text-to-image
Last push: 2023-11-15T15:00:12+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 90
- inputs: {"age_days": 1261, "days_push": 1022, "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 1089, forks 75 (observed 2026-08-28T04:03:32.663695+00:00)

## What it is
Text2Room is a research codebase that generates room-scale textured 3D meshes from a text prompt by leveraging pre-trained 2D text-to-image diffusion models, monocular depth estimation, and text-conditioned inpainting. It is the official implementation of the ICCV 2023 paper and outputs fused PLY meshes plus rendered scene images.

## Use cases
- generate a textured 3D room mesh from a text prompt
- create 3D scenes from text descriptions
- research text-to-3D scene generation with diffusion models
- produce room-scale 3D geometry for graphics experiments
- reproduce ICCV 2023 Text2Room paper results
- explore lifting 2D text-to-image outputs into 3D meshes

## When to choose
- you need text-to-3D room-scale mesh generation with explicit geometry
- you want a research baseline for lifting 2D diffusion outputs into 3D scenes
- you can run GPU-heavy Stable Diffusion and PyTorch3D pipelines

## When to avoid
- you need production-ready, fast, or interactive 3D generation
- you want real-time or game-ready asset pipelines without heavy GPU setup
- you need actively maintained software with frequent updates

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

## Member repositories
- lukasHoel/text2room (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:32.663695+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 2026-08-30T06:49:18.496432+00:00, confidence not recorded.
  - readme: https://github.com/lukasHoel/text2room (fetched 2026-08-28T04:03:32.663695+00:00, sha 35a52b66a422)
  - homepage: https://lukashoel.github.io/text-to-room/ (fetched 2026-08-29T12:51:47.800098+00:00, sha b8053eb43167)
- Data as of 2026-08-30T08:39:29.467469+00:00.