𝗠𝗖𝗠𝗟 𝗕𝗹𝗼𝗴: Images and text are usually aligned using millions of image–caption pairs. But could they still be matched if they were never seen together? In “It’s a (Blind) Match!”, MCML Members explore this question. mcml.ai/news/2026-01...

🦖 We present “Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion”. #ICCV2025 🌍: visinf.github.io/scenedino/ 📃: arxiv.org/abs/2507.06230 🤗: huggingface.co/spaces/jev-a... @jev-aleks.bsky.social @fwimbauer.bsky.social @olvrhhn.bsky.social @stefanroth.bsky.social @dcremers.bsky.social

The code for our #CVPR2025 paper, PRaDA: Projective Radial Distortion Averaging, is now out! Turns out distortion calibration from multiview 2D correspondences can be fully decoupled from 3D reconstruction, greatly simplifying the problem arxiv.org/abs/2504.16499 github.com/DaniilSinits...

Can we match vision and language representations without any supervision or paired data? Surprisingly, yes!  Our #CVPR2025 paper with @neekans.bsky.social and @dcremers.bsky.social shows that the pairwise distances in both modalities are often enough to find correspondences. ⬇️ 1/4

Can you train a model for pose estimation directly on casual videos without supervision? Turns out you can! In our #CVPR2025 paper AnyCam, we directly train on YouTube videos and achieve SOTA results by using an uncertainty-based flow loss and monocular priors! ⬇️

Check out our latest recent #CVPR2025 paper AnyCam, a fast method for pose estimation in casual videos! 1️⃣ Can be directly trained on casual videos without the need for 3D annotation. 2️⃣ Based around a feed-forward transformer and light-weight refinement. Code and more info: ⏩ fwmb.github.io/anycam/

We are thrilled to have 12 papers accepted to #CVPR2025. Thanks to all our students and collaborators for this great achievement! For more details check out cvg.cit.tum.de

Indeed - everyone had a blast - thank you all for the great talks, discussions and Ski/snowboarding!