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[CVPR 2025 Highlight] Official code for "Olympus: A Universal Task Router for Computer Vision Tasks"
| Date | Stars |
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| 2026-07-24 | 428 |
| 2026-07-25 | 428 |
| 2026-07-28 | 428 |
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| 2026-09-20 | 427 |
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<div align="center"> <img src="https://github.com/yuanze-lin/Olympus/blob/main/asset/olympus.png" alt="Olympus" width="130" height="130" /> # Olympus: A Universal Task Router for Computer Vision Tasks **CVPR 2025 (Highlight)** [](https://arxiv.org/pdf/2412.09612) [](https://arxiv.org/abs/2412.09612) [](https://yuanze-lin.me/Olympus_page/) [](https://huggingface.co/Yuanze/Olympus) [](https://huggingface.co/datasets/Yuanze/Olympus) [](https://www.youtube.com/watch?v=N1xOdIrVvn4) [Yuanze Lin](https://yuanze-lin.me/) · [Yunsheng Li](https://scholar.google.com/citations?user=hJrIyCwAAAAJ&hl=en) · [Dongdong Chen](https://www.dongdongchen.bid/) · [Weijian Xu](https://weijianxu.com/) · [Ronald Clark](https://www.ron-clark.com/) · [Philip H. S. Torr](https://eng.ox.ac.uk/people/philip-torr/) [**Installation**](#install) · [**Models & Data**](#data) · [**Inference**](#specialists) · [**Training**](#training) · [**Evaluation**](#evaluation) · [**Citation**](#citation) </div> Olympus routes a single natural-language instruction across **20 vision tasks**, dispatches each to a specialist model, and chains their outputs. One prompt in, finished `.png`, `.mp4` and `.glb` files out. **:hearts: If you find our project is helpful for your research, please kindly give us a :star2: and cite our paper :bookmark_tabs: : )** ## :mega: News - [x] **Prompt-to-Assets:** A single instruction now returns finished assets, `.png` / `.mp4` / `.glb`. - [x] Release the code for integration with task-specific models. - [x] Release the training & inference code. - [x] Release Olympus datasets. - [x] Release the model of Olympus. ## :low_brightness: Overview  ## :hammer_and_wrench: Installation <a href="#install" id="install"/> To establish the environment, just run this code in the shell: ``` git clone https://github.com/yuanze-lin/Olympus.git cd Olympus conda create -n olympus python==3.10 -y conda activate olympus pip install -r requirements.txt ``` That will create the environment ```olympus``` we used. That is all you need to run the router on its own. To also **execute** the routed tasks and generate real images, videos and 3D models with [`run_tools.py`](#specialists), install the specialist stack into the *same* environment: ``` pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124 pip install -r requirements_tools.txt bash scripts/install_specialists.sh ``` The router and every specialist share this one environment; there is no per-tool environment to manage. ### 3D backends `<3D_gen_image>` uses [TRELLIS.2-4B](https://huggingface.co/microsoft/TRELLIS.2-4B) and `<3D_gen_text>` uses [TRELLIS-text-base](https://huggingface.co/microsoft/TRELLIS-text-base), both producing textured meshes with PBR materials. They compile several CUDA extensions, so they install separately: ``` bash scripts/install_3d.sh ``` That builds both, plus Hunyuan3D-2 as an ungated fallback for `<3D_gen_image>`. Each step is independent, so a failure in one does not block the others, and any 3D token whose backend is missing falls back automatically rather than erroring. See the [3D fallbacks](d
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matched fp:a46fe7f49fe2f8b0, topic:multimodal, topic:vision-language-model
matched fp:a46fe7f49fe2f8b0, topic:pytorch
matched fp:a46fe7f49fe2f8b0, topic:instruction-tuning, readme:fine-tuning, readme:fine tuning
matched fp:a46fe7f49fe2f8b0, topic:chatbot, topic:chatgpt