An interesting talk by @ylecun at ECCV 2026 in Malmรถ! Lots of thought-provoking ideas on the future of AI and computer vision. ๐ค๐
For live visit: https://t.co/9rdStAv5UC
Excited to announce that our recent work โSelfGeoโ has been accepted at #ECCV2024, one of the top conferences in computer vision! ๐๐
Check out the paper ๐and code ๐using the links ๐below.
๐ Excited to unveil our latest #ECCV2024 paper: "SelfGeo: Self-supervised and Geodesic-consistent Estimation of Keypoints on Deformable Shapesโwhere we show how to extract keypoints from non-rigid point clouds without supervision ๐โจ
๐งต1/4
Join us now at @CVPR poster session (stand 379) and learn how we solve puzzles...and you can also challenge yourself with a real puzzle ๐งฉ๐งฉ๐งฉ
Project: https://t.co/motIsP5pDm
Paper: https://t.co/91PnnAdke6
#cvpr2024@IITalk
Do you feel stuck and want someone to guide you further but can't afford a MENTOR?
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https://t.co/gvmRVgAlQb
Young scientists regularly ask me for career advice. Academia or industry? Big company or startup? US or Europe?ย Good scientists in AI disciplines are fortunate to have many choices. But choosing can be stressful. I always give the same advice. 1/10
Our new @ieee_ras_icra 2024 shows we can estimate the camera pose from a #NeRF model at 34fps from a single image ๐๐๐ and without the need for a rotation and translation for initialization! ๐ก๐ก๐ก
๐ฌThis short video shows how we did it... ๐งต
Tired of dealing with relative camera poses made noisy by keypoint mismatches? Have a look at our #3dv2024 paper PRAGO, addressing this via object-based matches! Come and say hi to @matteo_taiana, @alessiodelbue, @sttujames and me tomorrow 16:30 to 18:00 at Poster Session 2!
๐งฉ Excited to unveil our latest #CVPR2024 paper! ๐ DiffAssemble: a groundbreaking diffusion model solving puzzle reassembly challenges in both 2D and 3D spaces. Bye-bye noise, hello perfect reconstruction!
๐ #GenAI#DiffusionModels
๐ Dive into the details with our thread!๐
๐ We inject time-dependent noise into translations and rotations, akin to shuffling elements. During training, our Attention-based GNN restores initial translations and rotations. At inference, elements start from noise and are iteratively denoised, forming a coherent structure.
Check out @chrdiller's CG-HOI :)
We generate realistic 3D human-object interactions, from object geometry and text description.
A key ingredient is explicit modeling of contact, during training and as guidance during inference.
https://t.co/Cl5Jw9oFBO
https://t.co/FVIFqEpjHi
๐ขWe are #hiring
Looking for 2 #postdocs and 1 technician to join our Lab๐ค in Genoa ๐ฎ๐น๐
Researchers with #EEG skills and #computational neuroscience background are very welcome to apply
Here the links:
https://t.co/yYR36C5uNy
https://t.co/xZWcSAzqN7
https://t.co/QZ6tRevZUh