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š„ RelateAnything is gold! š„
šRelateAnything is a 53M-parameter relation model that takes an image and a set of regions from any source and returns scored relations over a predicate vocabulary supplied at inference as a list of strings. Impressive results. Repo under Apache 2.0š
šReview https://lnkd.in/p/etAcdFM3
šPaper https://arxiv.org/pdf/2609.12552
šRepo https://github.com/Maelic/RelateAnything
šProject https://maelic.github.io/RelateAnythingProject/
šRelateAnything is a 53M-parameter relation model that takes an image and a set of regions from any source and returns scored relations over a predicate vocabulary supplied at inference as a list of strings. Impressive results. Repo under Apache 2.0š
šReview https://lnkd.in/p/etAcdFM3
šPaper https://arxiv.org/pdf/2609.12552
šRepo https://github.com/Maelic/RelateAnything
šProject https://maelic.github.io/RelateAnythingProject/
ā¤12š„4š3š1š¤Æ1
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š EventEgoHands++ is out! š
šEventEgoHands++ is a novel framework for event-based 3D hand mesh reconstruction from an egocentric viewpoint. 1M+ samples dataset! Code/Data releasedš
šReview https://lnkd.in/p/eTbPvXbW
šPaper https://arxiv.org/pdf/2609.17189
šRepo https://github.com/ryhara/EventEgoHandsV2
šProject https://ryhara.github.io/EventEgoHandsV2/
šEventEgoHands++ is a novel framework for event-based 3D hand mesh reconstruction from an egocentric viewpoint. 1M+ samples dataset! Code/Data releasedš
šReview https://lnkd.in/p/eTbPvXbW
šPaper https://arxiv.org/pdf/2609.17189
šRepo https://github.com/ryhara/EventEgoHandsV2
šProject https://ryhara.github.io/EventEgoHandsV2/
ā¤3š„2š1
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š¦SOTA Splashing Liquidsš¦
šSplashSplat reconstructs splashing liquids from real multi-view vide. Impose physical structure only where the observations can constrain it. Impressive results, SOTA. Code TBR under MITš
šReview https://lnkd.in/p/ejMTHcp7
šPaper https://arxiv.org/pdf/2609.20818
šProject niko-creater.github.io/splashsplat-web/
šRepo https://github.com/Niko-creater/Splashsplat
šSplashSplat reconstructs splashing liquids from real multi-view vide. Impose physical structure only where the observations can constrain it. Impressive results, SOTA. Code TBR under MITš
šReview https://lnkd.in/p/ejMTHcp7
šPaper https://arxiv.org/pdf/2609.20818
šProject niko-creater.github.io/splashsplat-web/
šRepo https://github.com/Niko-creater/Splashsplat
š4ā¤2š„2š1
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š„Agentic Image-to-Sceneš„
šHARMONY by UPenn is a hierarchical chain-of-thought framework that leverages both agentic reasoning and visual geometry foundation. Impressive 3D scenes. Repo TBAš
šReview https://lnkd.in/p/ep2hmRSp
šPaper https://arxiv.org/pdf/2609.26793
šProject https://cwchenwang.github.io/harmony/
šData https://huggingface.co/datasets/ShufanSun/harmony
šHARMONY by UPenn is a hierarchical chain-of-thought framework that leverages both agentic reasoning and visual geometry foundation. Impressive 3D scenes. Repo TBAš
šReview https://lnkd.in/p/ep2hmRSp
šPaper https://arxiv.org/pdf/2609.26793
šProject https://cwchenwang.github.io/harmony/
šData https://huggingface.co/datasets/ShufanSun/harmony
š„8ā¤2š1
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šæPanoSeg3R: SOTA 3D Segmentationšæ
šPanoSeg3R is a novel feed-forward framework for 3D panoramic semantic segmentation. New SOTA. Code comingš
šReview https://lnkd.in/p/eKCKWv3g
šPaper https://arxiv.org/pdf/2609.22687
šProject https://harryyoon777.github.io/PanoSeg3R/#
šRepo TBA
šPanoSeg3R is a novel feed-forward framework for 3D panoramic semantic segmentation. New SOTA. Code comingš
šReview https://lnkd.in/p/eKCKWv3g
šPaper https://arxiv.org/pdf/2609.22687
šProject https://harryyoon777.github.io/PanoSeg3R/#
šRepo TBA
ā¤5š1š„1š1
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š©»Universal X-ray Segmentationš©»
šFleXray: universal anatomical segmentation across the entire body in clinical X-rays. Built on a scalable, physics-based generative X-ray data engine. Repo under MITš
šReview https://lnkd.in/p/e9MUk_eq
šPaper https://arxiv.org/pdf/2609.26756
šProject https://flexray.csail.mit.edu/
šRepo https://github.com/VictorButoi/FleXray
šFleXray: universal anatomical segmentation across the entire body in clinical X-rays. Built on a scalable, physics-based generative X-ray data engine. Repo under MITš
šReview https://lnkd.in/p/e9MUk_eq
šPaper https://arxiv.org/pdf/2609.26756
šProject https://flexray.csail.mit.edu/
šRepo https://github.com/VictorButoi/FleXray
š5ā¤3š„3š2š¤Æ1
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š¦“3D Foundational Radiologyš¦“
šnnFoundation: 3D radiological foundation models designed for transferable representation learning across heterogeneous tasks/datasets. Models releasedš
šReview https://lnkd.in/p/eNajRGBi
šPaper https://arxiv.org/pdf/2609.26924
šModels https://huggingface.co/collections/MIC-DKFZ/nnfoundation
šnnFoundation: 3D radiological foundation models designed for transferable representation learning across heterogeneous tasks/datasets. Models releasedš
šReview https://lnkd.in/p/eNajRGBi
šPaper https://arxiv.org/pdf/2609.26924
šModels https://huggingface.co/collections/MIC-DKFZ/nnfoundation
ā¤9š2š2š„1š¤©1
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š„TrackEverything is outš„
šTrackEverything is the first 3D point tracker capable of tracking all visible points across long horizons (1000+ frames). Repo announcedš
šReview https://lnkd.in/p/eCPJ6h2B
šPaper https://arxiv.org/pdf/2609.30222
šProject https://trackeverything.github.io/
šRepo https://github.com/ayushjain1144/trackeverything
šTrackEverything is the first 3D point tracker capable of tracking all visible points across long horizons (1000+ frames). Repo announcedš
šReview https://lnkd.in/p/eCPJ6h2B
šPaper https://arxiv.org/pdf/2609.30222
šProject https://trackeverything.github.io/
šRepo https://github.com/ayushjain1144/trackeverything
ā¤6š„6š2š1
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š„Ego-Exo4D Human Datasetš„
šForm the University of Austin, Ego-Exo4D-HM: large-scale dataset of 4D human motion reconstructions for Ego-Exo4Dās captures + reconstruction pipeline. Code, dataset, and docs š
šReview https://lnkd.in/p/eVFt9jPr
šPaper https://lnkd.in/eWj4cD7T
šProject https://lnkd.in/euPqVNxV
šForm the University of Austin, Ego-Exo4D-HM: large-scale dataset of 4D human motion reconstructions for Ego-Exo4Dās captures + reconstruction pipeline. Code, dataset, and docs š
šReview https://lnkd.in/p/eVFt9jPr
šPaper https://lnkd.in/eWj4cD7T
šProject https://lnkd.in/euPqVNxV
1ā¤5š„3š1
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š„š„ 70,000+ š„š„
š Crazy how a boring science project (no kittens, no rants, no personal dramas) can reach for 70,000+ followers. Speechless.
Love u š
š https://lnkd.in/p/eD6Xxdxi
š Crazy how a boring science project (no kittens, no rants, no personal dramas) can reach for 70,000+ followers. Speechless.
Love u š
š https://lnkd.in/p/eD6Xxdxi
ā¤26š¾8š„3ā”2š2š2š¤Æ1
š„The Computer Vision ultimate collectionš„
šStan Birchfield (#Nvidia) just dropped this on arXiv. From classical image processing and 3D geometry to CNNs, Transformers, foundation models, and neural rendering. What makes this book damn good is the combination of clear explanations and working Python. A gift.
šReview https://lnkd.in/p/ejwm_DVn
šBook https://lnkd.in/eTrEvmd9
šCode https://lnkd.in/eakj9VZU
šStan Birchfield (#Nvidia) just dropped this on arXiv. From classical image processing and 3D geometry to CNNs, Transformers, foundation models, and neural rendering. What makes this book damn good is the combination of clear explanations and working Python. A gift.
šReview https://lnkd.in/p/ejwm_DVn
šBook https://lnkd.in/eTrEvmd9
šCode https://lnkd.in/eakj9VZU
ā¤26š„8š2š©1š1
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šPhysically Plausible 3D Motionš
šPhysically plausible motion recovery: given a monocular video, FlowHMR recovers global 3D human motion that a physics-based controller can successfully track in simulation. Repoš
šReview https://lnkd.in/p/d77fzUtR
šPaper https://arxiv.org/pdf/2610.03691
šProject https://flowhmr.github.io/
šRepo https://github.com/flowhmr/flowhmr
šPhysically plausible motion recovery: given a monocular video, FlowHMR recovers global 3D human motion that a physics-based controller can successfully track in simulation. Repoš
šReview https://lnkd.in/p/d77fzUtR
šPaper https://arxiv.org/pdf/2610.03691
šProject https://flowhmr.github.io/
šRepo https://github.com/flowhmr/flowhmr
ā¤6š„4š1
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šØ Rome from ONE pic š„
šDetailed scene meshes from one photograph: the method completes geometry beyond the observed view and supports indoor, outdoor, and large-scale scenes. Repo announcedš
šReview https://lnkd.in/p/eMdWBEGn
šPaper https://arxiv.org/pdf/2610.08790
šProject https://build-rome.github.io/
šRepo TBA
šDetailed scene meshes from one photograph: the method completes geometry beyond the observed view and supports indoor, outdoor, and large-scale scenes. Repo announcedš
šReview https://lnkd.in/p/eMdWBEGn
šPaper https://arxiv.org/pdf/2610.08790
šProject https://build-rome.github.io/
šRepo TBA
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