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Running on Zero
| title: Marigold Normals Estimation | |
| emoji: 🏵️ | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 4.44.1 | |
| app_file: app.py | |
| pinned: true | |
| license: apache-2.0 | |
| models: | |
| - prs-eth/marigold-normals-v1-1 | |
| This is a demo of Marigold, the state-of-the-art normals estimator for images in the wild. | |
| Find out more in our paper titled ["Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis"](https://arxiv.org/abs/2505.09358) | |
| ``` | |
| @InProceedings{ke2023repurposing, | |
| title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation}, | |
| author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler}, | |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| year={2024} | |
| } | |
| @misc{ke2025marigold, | |
| title={Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis}, | |
| author={Bingxin Ke and Kevin Qu and Tianfu Wang and Nando Metzger and Shengyu Huang and Bo Li and Anton Obukhov and Konrad Schindler}, | |
| year={2025}, | |
| eprint={2505.09358}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` | |