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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: 🌐 Website: 🤗 Hugging Face Space: 📄 Paper: 👾 Code:...

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Anton Obukhov

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490,031 görüntüleme • 2 yıl önce •via X (Twitter)

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AI葵(Aoi) 🇹🇼NeRF技術型Vtuber profil fotoğrafı
AI葵(Aoi) 🇹🇼NeRF技術型Vtuber2 yıl önce

70s of inference for an image is very slow..

Anton Obukhov profil fotoğrafı
Anton Obukhov2 yıl önce

We focus on the quality first - speed will come later through research and improvements in diffusion samplers. Arguably, Stable Diffusion is also slower than most of the GANs :)

Bilawal Sidhu profil fotoğrafı
Bilawal Sidhu2 yıl önce

Damn those are some crispy depth maps!

Chris Offner profil fotoğrafı
Chris Offner2 yıl önce

Wow, those depth maps look amazing. Great work! There goes my course project for Deep Learning. 😅

Boyuan Chen profil fotoğrafı
Boyuan Chen2 yıl önce

Is this metric or relative depth?

Anton Obukhov profil fotoğrafı
Anton Obukhov2 yıl önce

Relative

Janek Mann profil fotoğrafı
Janek Mann2 yıl önce

I also consider this strong evidence that Stable Diffusion learned much stronger 3D scene understanding than commonly assumed.

Omar Sanseviero profil fotoğrafı
Omar Sanseviero2 yıl önce

Very very cool!

Tom profil fotoğrafı
Tom2 yıl önce

Wow. More detailed than the ground truth, very cool to see.

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Pixel-Perfect-Depth: the paper aims to fix Marigold's loss of sharpness induced by VAE by using VFMs (VGGT/DAv2) and a DiT-based pixel decoder to refine the predictions and achieve clean depth discontinuities. Video by authors.

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We go even more in-depth in the full version on wired dot com and on youtube, check it out!!
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We go even more in-depth in the full version on wired dot com and on youtube, check it out!!

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