Another gem from our lab — DGInStyle! We use Stable Diffusion to generate semantic segmentation data for autonomous driving and train domain-generalizable networks.
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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.
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📟 Website: 
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📟 Website: 
https://dginstyle.github.io
https://dginstyle.github.io
link
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🧾 Paper: 
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🧾 Paper: 
https://arxiv.org/abs/2312.03048
https://arxiv.org/abs/2312.03048
link
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🤗 Hugging Face Paper: 
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🤗 Hugging Face Paper: 
DGInStyle: Domain-Generalizable Semantic Segmentation with Image
  Diffusion Models and Stylized Semantic Control (2312.03048)
https://huggingface.co/papers/2312.03048
2312.03048
paper
resource
https://huggingface.co/papers/2312.03048
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🤗 Hugging Face Model: 
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🤗 Hugging Face Model: 
https://huggingface.co/yurujaja/DGInStyle
yurujaja/DGInStyle
model
resource
https://huggingface.co/yurujaja/DGInStyle
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🐙 Code: 
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🐙 Code: 
https://github.com/yurujaja/DGInStyle
https://github.com/yurujaja/DGInStyle
link
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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. 
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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. 
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Team: Yuru Jia (
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Team: Yuru Jia (
@yurujaja
mention
yurujaja
), Lukas Hoyer, Shengyu Huang, Tianfu Wang (
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), Lukas Hoyer, Shengyu Huang, Tianfu Wang (
@Tianfwang
mention
Tianfwang
), Luc Van Gool, Konrad Schindler, and Anton Obukhov (
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), Luc Van Gool, Konrad Schindler, and Anton Obukhov (
@toshas
mention
toshas
).
text
).
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: https://huggingface.co/papers/2312.03048
🤗 Hugging Face Model: https://huggingface.co/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).
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