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Anton Obukhov

toshas
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AntonObukhov1
toshas

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posted an update 9 days ago
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Join us at our remaining CVPR presentations this week! Members of PRS-ETH will be around to connect with you and discuss our presented and ongoing works:

💐 Marigold: Discover our work on sharp diffusion-based computer vision techniques, presented in Orals 3A track on "3D from Single View", Thu, June 20, 9:00-9:15 AM. Also, drop by Poster Session 3 later that day for more tangible matters! 🌚
Project page: https://marigoldmonodepth.github.io/
Paper: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation (2312.02145)
Collection: https://huggingface.co/collections/prs-eth/marigold-6669e9e3d3ee30f48214b9ba
Space: prs-eth/marigold-lcm
Diffusers 🧨 tutorial: https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage

⚙️ Point2CAD: Learn about our mechanical CAD model reconstruction from point clouds, presented in Poster Session 1, Wed, June 19, 10:30 AM - 12:00 PM.
Project page: https://www.obukhov.ai/point2cad.html
Paper: Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds (2312.04962)

🎭 DGInStyle: Explore our generative data synthesis approach as a cost-efficient alternative to real and synthetic data, presented in the Workshop on Synthetic Data for Computer Vision, Tue, June 18, at Summit 423-425.
Details and schedule: https://syndata4cv.github.io/
Project page: https://dginstyle.github.io/
Paper: DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control (2312.03048)
Model: yurujaja/DGInStyle
posted an update 3 months ago
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Another gem from our lab — DGInStyle! We use Stable Diffusion to generate semantic segmentation data for autonomous driving and train domain-generalizable networks.

📟 Website: https://dginstyle.github.io
🧾 Paper: https://arxiv.org/abs/2312.03048
🤗 Hugging Face Paper: DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control (2312.03048)
🤗 Hugging Face Model: yurujaja/DGInStyle
🐙 Code: https://github.com/yurujaja/DGInStyle

In a nutshell, our pipeline overcomes the resolution loss of Stable Diffusion latent space and the style bias of ControlNet, as shown in the attached figures. This allows us to generate sufficiently high-quality pairs of images and semantic masks to train domain-generalizable semantic segmentation networks.

Team: Yuru Jia ( @yurujaja ), Lukas Hoyer, Shengyu Huang, Tianfu Wang ( @Tianfwang ), Luc Van Gool, Konrad Schindler, and Anton Obukhov ( @toshas ).
posted an update 3 months ago
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Introducing Marigold-LCM 🌼 — a FAST version of the now popular state-of-the-art depth estimator! Thanks to the latent consistency distillation, it retains the precision of the original Marigold but reaches the solution in just a few steps!

Check out the teaser video attached below and play with the new demo - it accepts videos now! Also, meet the new team member: Tianfu Wang ( @Tianfwang )

🤗 Demo: prs-eth/marigold-lcm
🤗 Model: https://huggingface.co/prs-eth/marigold-lcm-v1-0
🤗 Original Marigold post: https://huggingface.co/posts/toshas/656973498012745
🤗 Paper: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation (2312.02145)
🌐 Website: https://marigoldmonodepth.github.io
👾 Code: https://github.com/prs-eth/marigold
👾 Code: pip install diffusers
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replied to their post 6 months ago
posted an update 6 months ago
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Introducing Marigold 🌼 - a universal monocular depth estimator, delivering incredibly sharp predictions in the wild! Based on Stable Diffusion, it is trained with synthetic depth data only and excels in zero-shot adaptation to real-world imagery. Check it out:

🤗 Hugging Face Space: https://huggingface.co/spaces/toshas/marigold
🤗 Hugging Face Model: https://huggingface.co/Bingxin/Marigold
🤗 Hugging Face Paper: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation (2312.02145)
🌐 Website: https://marigoldmonodepth.github.io
👾 Code: https://github.com/prs-eth/marigold
👾 Code: pip install diffusers (check comments to this post for details!)
📄 Paper: https://arxiv.org/abs/2312.02145

Brought to you by the fantastic team from the Photogrammetry and Remote Sensing group of ETH Zurich: Bingxin Ke ( @Bingxin ), Anton Obukhov ( @toshas ), Shengyu Huang, Nando Metzger ( @nandometzger ), Rodrigo Caye Daudt, and Konrad Schindler.
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