--- title: "MoGe 2" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6fce963a-bcfa-443a-8298-3917e0d52047" updated: "2026-11-12" --- # MoGe 2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Estimates the geometry of a scene at true scale from a single image, in a Gradio demo you open in the browser. Suits researchers and developers working with depth and shape from photos. MoGe 2 is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Ruicheng ## About ### What it is MoGe 2 estimates the shape of a scene from one picture, and it does so at metric scale, meaning in physical units. This Space is a Gradio demo of that model. You open it in a browser with nothing to install. The source gives only a one-line summary, so the exact outputs are shown on the page. ### Who it suits It suits computer vision researchers and developers who need depth or scene structure from ordinary photos. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Computer vision | Researchers | Try single-image geometry estimation on their own pictures | Upload a photo and inspect the estimated geometry | | Robotics | Engineers | See what depth information one camera frame can give | Test a frame from a robot's camera | | Graphics and games | Artists | Get a rough scene structure to start from | Try an example image to see the result | | Software | Developers | Read how the model is wrapped in a demo and a package | Open app.py and the moge folder | Skip it if you need several photos fused into one model, since the source describes single-image estimation. ### Where to use it - A browser: open the Space's page on Hugging Face, with nothing to install - A copy on Windows, macOS or Linux, using Python | Use case | Who | What they get back | | --- | --- | --- | | Estimating geometry | Researchers | A geometry estimate from one picture | | Trying the examples | Newcomers | Results for the example images in the repository | | Reading the code | Developers | A package with configs, baselines and docs | ### How to set it up The online copy needs nothing. For a copy on your own computer, you need the following. - Python with pip on Windows, macOS or Linux - The Hugging Face hub client, which the first step installs - The packages listed in requirements.txt 1. Install the Hugging Face hub client. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Install the Gradio version the Space pins. 5. Install the remaining packages from requirements.txt. 6. Start app.py. ```sh pip install -U huggingface_hub hf download Ruicheng/MoGe-2 --repo-type space --local-dir MoGe-2 cd MoGe-2 pip install gradio==5.33.0 pip install -r requirements.txt python app.py ``` ### How to use it Online, open the Space's page, give it a picture and run it. On your own computer, start the app with the command below and use the page it launches. The source does not describe the controls, so follow the labels. ```sh python app.py ``` ### Example You upload a photo of a room and run the model. The Space returns an estimate of the room's geometry that you can inspect. The source does not say which views or file types it offers. ### For newcomers and experts If you are new, try one of the example images in the repository first. Then use a photo of your own with a clear scene and good light. If you know the field, the Space names two checkpoints, a ViT-L model with a normal output and an earlier ViT-L model. The package is built to estimate geometry at metric scale from one image. The Space runs on zero-a10g hardware at Hugging Face. You open the Space or run app.py → You give it one picture → The MoGe model estimates the geometry → You inspect the result ### Good to know The source is short, so accuracy and limits are not described. - License: mit, as listed on Hugging Face - Cost to run: no API key or paid service is named, and the online copy runs on zero-a10g hardware at Hugging Face - Data it reaches: the pictures you upload, which the online Space handles on Hugging Face's servers, and a local copy downloads the Space's files - Not for: the source lists no exclusions, so use only pictures you have the right to use ## README ````markdown # MoGe 2 Estimates the geometry of a scene at true scale from a single image, in a Gradio demo you open in the browser. Suits researchers and developers working with depth and shape from photos. Made by Ruicheng on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: MoGe 2 estimates the shape of a scene from one picture, and it does so at metric scale, meaning in physical units. This Space is a Gradio demo of that model. You open it in a browser with nothing to install. The source gives only a one-line summary, so the exact outputs are shown on the page. Who it suits: It suits computer vision researchers and developers who need depth or scene structure from ordinary photos. ```table Industry | Who uses it | How they use it | Example Computer vision | Researchers | Try single-image geometry estimation on their own pictures | Upload a photo and inspect the estimated geometry Robotics | Engineers | See what depth information one camera frame can give | Test a frame from a robot's camera Graphics and games | Artists | Get a rough scene structure to start from | Try an example image to see the result Software | Developers | Read how the model is wrapped in a demo and a package | Open app.py and the moge folder ``` Skip it if you need several photos fused into one model, since the source describes single-image estimation. Where to use it: - A browser: open the Space's page on Hugging Face, with nothing to install - A copy on Windows, macOS or Linux, using Python ```table Use case | Who | What they get back Estimating geometry | Researchers | A geometry estimate from one picture Trying the examples | Newcomers | Results for the example images in the repository Reading the code | Developers | A package with configs, baselines and docs ``` How to set it up: The online copy needs nothing. For a copy on your own computer, you need the following. - Python with pip on Windows, macOS or Linux - The Hugging Face hub client, which the first step installs - The packages listed in requirements.txt 1. Install the Hugging Face hub client. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Install the Gradio version the Space pins. 5. Install the remaining packages from requirements.txt. 6. Start app.py. ```sh pip install -U huggingface_hub hf download Ruicheng/MoGe-2 --repo-type space --local-dir MoGe-2 cd MoGe-2 pip install gradio==5.33.0 pip install -r requirements.txt python app.py ``` How to use it: Online, open the Space's page, give it a picture and run it. On your own computer, start the app with the command below and use the page it launches. The source does not describe the controls, so follow the labels. ```sh python app.py ``` Example: You upload a photo of a room and run the model. The Space returns an estimate of the room's geometry that you can inspect. The source does not say which views or file types it offers. For newcomers and experts: If you are new, try one of the example images in the repository first. Then use a photo of your own with a clear scene and good light. If you know the field, the Space names two checkpoints, a ViT-L model with a normal output and an earlier ViT-L model. The package is built to estimate geometry at metric scale from one image. The Space runs on zero-a10g hardware at Hugging Face. ```flow You open the Space or run app.py You give it one picture The MoGe model estimates the geometry You inspect the result ``` Good to know: The source is short, so accuracy and limits are not described. - License: mit, as listed on Hugging Face - Cost to run: no API key or paid service is named, and the online copy runs on zero-a10g hardware at Hugging Face - Data it reaches: the pictures you upload, which the online Space handles on Hugging Face's servers, and a local copy downloads the Space's files - Not for: the source lists no exclusions, so use only pictures you have the right to use ## On Hugging Face, 13 November 2026 - Likes: 96 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/Ruicheng/MoGe-2 - Files: https://huggingface.co/spaces/Ruicheng/MoGe-2/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [Ruicheng/MoGe-2 on Hugging Face](https://huggingface.co/spaces/Ruicheng/MoGe-2) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | MoGe 2 (this page) | 96 | gradio | mit | 2025-07-01 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-07-01 - First published: 2025-06-09 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Ruicheng/MoGe-2 on Hugging Face](https://huggingface.co/spaces/Ruicheng/MoGe-2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6fce963a-bcfa-443a-8298-3917e0d52047) - [Its page on Mindket](https://mindket.com/p/6fce963a-bcfa-443a-8298-3917e0d52047)