Anton Obukhov


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Another gem from our lab โ€” DGInStyle! We use Stable Diffusion to generate semantic segmentation data for autonomous driving and train domain-generalizable networks.

๐Ÿ“Ÿ Website:
๐Ÿงพ Paper:
๐Ÿค— Hugging Face Paper: DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control (2312.03048)
๐Ÿค— Hugging Face Model: yurujaja/DGInStyle
๐Ÿ™ Code:

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 ).
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Introducing Marigold-LCM ๐ŸŒผ โ€” 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 )

๐Ÿค— Demo: prs-eth/marigold-lcm
๐Ÿค— Model: prs-eth/marigold-lcm-v1-0
๐Ÿค— Original Marigold post:
๐Ÿค— Paper: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation (2312.02145)
๐ŸŒ Website:
๐Ÿ‘พ Code:
๐Ÿ‘พ Code: pip install diffusers


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