NeuralFur wins Best Paper Runner Up at @3DVconf. From multi-view images, we create a strand-based hair groom for animals. Unlike human hair, fur varies in length across the body parts of animals.
NeuralFur leverages a VQA approach to infer fur lengths and directions across the body and to create a furless mesh.
We then reconstruct strand-based fur geometry from multi-view images, resulting in a realistic animal model that is ready for physics-based animation in game engines like Unreal.
Code is online. Check out the project page link below.
Congratulations to @ness_pirs@bernakabadayi@AYiannakidis@gfgbec and @JustusThies!
https://t.co/Xj6MV220e0
Excited and deeply honored to receive the #3D Outstanding Doctoral Dissertation Award! 🏆 Huge thanks to the awards committee and to everyone making 3DV such a blast.
And I couldn't be happier to share this recognition with my long-time friend and colleague @songyoupeng 🙌✨
Presenting M2SVid at #3DV2026 today! We tackle Monocular-to-Stereo Video conversion using a end-to-end inpainting and refinement pipeline.
📍 Poster Session 1 (10:15 - 12:00)
🚀 Code and models are now released!
🔗 https://t.co/PibIzp5RzI
💻 https://t.co/6ZkHc2C7dg…
@3DVconf
🚨Call for Extended Abstracts for the WiCV Women in Computer Vision workshop at @CVPR 2026.
Find all the details at: https://t.co/VdKr6mdVnU
Submission Portal: https://t.co/8HfFUtzQ6c
Deadline: 24 March 2026
#CVPR2026#WiCV#ComputerVision
🎆 Wrapping up 2025 with a review of some exciting papers from our group 🎊 covering everything across audio-visual learning, explainability, bias mitigation & video understanding 🧵⬇️
Excited to share our new paper: M2SVid: End-to-End Inpainting and Refinement for Monocular-to-Stereo Video Conversion! ACCEPTED by 3DV 2026!🎬
👉 Project: https://t.co/sLgaM9oMYV
📄 Paper: https://t.co/b8yPeyoK8o
Done with Goutam Bhat, @prunetruong, @HildeKuehne, @fedassa 🧵👇
📊 Results:
✅Higher Quality: Our approach outperforms previous state-of-the-art methods, being ranked best 2.6x more often than the second-place method in user studies.
✅Faster: Runs 6x faster than state-of-the-art competitors.
🚀 UTD is now fully released!
Code ✅ Models ✅ 2M video descriptions ✅ Debiased splits for 12 datasets ✅
Everything you need to benchmark video models more fairly is now public:
🔗 https://t.co/rA88ztIZJs
🎥 Let’s make video understanding actually about video understanding.
Can diffusion models solve visual Sudoku?
If you are at #ICML2025, come to our poster in the Wednesday morning poster session (Poster Session 3 East, Poster 3412) and find out!
@ChrisWewer@bartek_pog Bernt Schiele @janericlenssen
Check out our @CVPR poster!🎉
🕓 June 15, 4–6 PM
🖼️ Poster #278 | 📍ExHall D
Unbiasing through Textual Descriptions — we address representation bias in video benchmarks, releasing debiased splits for 12 datasets!
Can’t present in person due to visa issues, but I’ll be online! 💻