{"id":20573566,"url":"https://github.com/timf34/textto3dscene","last_synced_at":"2025-10-05T19:07:35.332Z","repository":{"id":232202779,"uuid":"778558838","full_name":"timf34/TextTo3DScene","owner":"timf34","description":null,"archived":false,"fork":false,"pushed_at":"2024-06-21T06:53:41.000Z","size":9005,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-16T21:25:29.271Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/timf34.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-03-28T00:13:24.000Z","updated_at":"2024-06-21T06:53:45.000Z","dependencies_parsed_at":"2025-01-16T21:23:56.820Z","dependency_job_id":"413de5ea-ebfe-4f34-b620-d4a85bcd9243","html_url":"https://github.com/timf34/TextTo3DScene","commit_stats":null,"previous_names":["timf34/textto3dscene"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timf34%2FTextTo3DScene","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timf34%2FTextTo3DScene/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timf34%2FTextTo3DScene/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timf34%2FTextTo3DScene/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/timf34","download_url":"https://codeload.github.com/timf34/TextTo3DScene/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242189711,"owners_count":20086612,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-16T05:28:08.820Z","updated_at":"2025-10-05T19:07:30.297Z","avatar_url":"https://github.com/timf34.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TextTo3DScene \n\nTextTo3DScene generates textured 3D meshes from a given text prompt using 2D text-to-image models, specifcally of \nrooms.\n\nThis project builds off the `text2room` [project](https://github.com/lukasHoel/text2room).\n\n\n## Prepare Environment\n\nCreate a conda environment:\n\n_Note: we need to delete/ unset PYTHONHOME in order for miniconda to work_\n\n```\nconda create -n text2room python=3.9\nconda activate text2room\npip install -r requirements.txt\n```\n\nThen install Pytorch3D by following the [official instructions](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md).\nFor example, to install Pytorch3D on Linux (tested with PyTorch 1.13.1, CUDA 11.7, Pytorch3D 0.7.2):\n\n```\nconda install -c fvcore -c iopath -c conda-forge fvcore iopath\npip install \"git+https://github.com/facebookresearch/pytorch3d.git@stable\"\n```\n\nDownload the pretrained model weights for the fixed depth inpainting model, that we use:\n\n- refer to the [official IronDepth implemention](https://github.com/baegwangbin/IronDepth) to download the files ```normal_scannet.pt``` and ```irondepth_scannet.pt```.\n- place the files under ```text2room/checkpoints```\n\n\n## Usage \n\nYou will need an API key, which can be placed in `keys.py` and set to a variable named `OPENAI_API_KEY`. \n\nRun `python generate_scene.py`\n\nThe mesh output will be saved to the `output/` directory along with other artifacts. We care about the `.ply` file \nthat ends with `quadric`. \n\n---\n\n(README below is from the project linked above and which we built on top of) \n\n---\n\n# Text2Room\nText2Room generates textured 3D meshes from a given text prompt using 2D text-to-image models.\n\nThis is the official repository that contains source code for the ICCV 2023 paper [Text2Room](https://lukashoel.github.io/text-to-room/).\n\n[[arXiv](https://arxiv.org/abs/2303.11989)] [[Project Page](https://lukashoel.github.io/text-to-room/)] [[Video](https://youtu.be/fjRnFL91EZc)]\n\n![Teaser](docs/teaser.jpg \"Text2Room\")\n\nIf you find Text2Room useful for your work please cite:\n```\n@InProceedings{hoellein2023text2room,\n    author    = {H\\\"ollein, Lukas and Cao, Ang and Owens, Andrew and Johnson, Justin and Nie{\\ss}ner, Matthias},\n    title     = {Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models},\n    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},\n    month     = {October},\n    year      = {2023},\n    pages     = {7909-7920}\n}\n```\n\n## Prepare Environment\n\nCreate a conda environment:\n\n_Note: we need to delete/ unset PYTHONHOME in order for miniconda to work_\n\n```\nconda create -n text2room python=3.9\nconda activate text2room\npip install -r requirements.txt\n```\n\nThen install Pytorch3D by following the [official instructions](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md).\nFor example, to install Pytorch3D on Linux (tested with PyTorch 1.13.1, CUDA 11.7, Pytorch3D 0.7.2):\n\n```\nconda install -c fvcore -c iopath -c conda-forge fvcore iopath\npip install \"git+https://github.com/facebookresearch/pytorch3d.git@stable\"\n```\n\nDownload the pretrained model weights for the fixed depth inpainting model, that we use:\n\n- refer to the [official IronDepth implemention](https://github.com/baegwangbin/IronDepth) to download the files ```normal_scannet.pt``` and ```irondepth_scannet.pt```.\n- place the files under ```text2room/checkpoints```\n\n(Optional) Download the pretrained model weights for the text-to-image model:\n\n- ```git clone https://huggingface.co/stabilityai/stable-diffusion-2-inpainting```\n- ```git clone https://huggingface.co/stabilityai/stable-diffusion-2-1```\n- ```ln -s \u003cpath/to/stable-diffusion-2-inpainting\u003e checkpoints```\n- ```ln -s \u003cpath/to/stable-diffusion-2-1\u003e checkpoints```\n\n## Generate a Scene\n\nAs default, we generate a living room scene:\n\n```python generate_scene.py```\n\nOutputs are stored in ```text2room/output```.\n\nSee the `text2room` repo for more instructions on customizing the mesh generation. \n\n## Notes on our implementation \n\nEmpirically we found the best results by... (explain how we attenuated the text2room pipeline)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimf34%2Ftextto3dscene","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftimf34%2Ftextto3dscene","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimf34%2Ftextto3dscene/lists"}