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ð Anyone in 4D is out ð
ð4DAnyone turns a casual monocular video into multi-view videos, enabling downstream 4DGS reconstruction. Full repo under Apache 2.0ð
ðReview https://lnkd.in/p/ec4dzGvb
ðPaper https://arxiv.org/pdf/2608.20335
ðProject https://4danyone.github.io
ðRepo github.com/ant-research/4DAnyone
ð4DAnyone turns a casual monocular video into multi-view videos, enabling downstream 4DGS reconstruction. Full repo under Apache 2.0ð
ðReview https://lnkd.in/p/ec4dzGvb
ðPaper https://arxiv.org/pdf/2608.20335
ðProject https://4danyone.github.io
ðRepo github.com/ant-research/4DAnyone
ðĨ7âĪ4ð1ð1
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ðâðĐRemesh-Aware Mesh DeformationðâðĐ
ðRADmesh is a novel generative deformation technique enhanced by remeshing. Given a text prompt, it deforms and remeshes a mesh region to form new geometric features. Repo MITð
ðReview https://lnkd.in/p/eK6FZv9c
ðPaper https://arxiv.org/pdf/2608.17182
ðProject https://threedle.github.io/radmesh/
ðRepo https://github.com/threedle/radmesh/
ðRADmesh is a novel generative deformation technique enhanced by remeshing. Given a text prompt, it deforms and remeshes a mesh region to form new geometric features. Repo MITð
ðReview https://lnkd.in/p/eK6FZv9c
ðPaper https://arxiv.org/pdf/2608.17182
ðProject https://threedle.github.io/radmesh/
ðRepo https://github.com/threedle/radmesh/
âĪ6ðĨ3ð1ð1
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ðĶUnified Segmentation n' RetrievalðĶ
ðFoundYou gets an example of your object and it segments the same physical instance in a new image or retrieve it from a large gallery with ONE super-compact model. Repo/demo availableð
ðReview https://lnkd.in/p/ex2qnKHW
ðPaper arxiv.org/pdf/2608.29917
ðProject https://lnkd.in/eNEUB_nV
ðRepo https://lnkd.in/eRyDY6Ue
ðFoundYou gets an example of your object and it segments the same physical instance in a new image or retrieve it from a large gallery with ONE super-compact model. Repo/demo availableð
ðReview https://lnkd.in/p/ex2qnKHW
ðPaper arxiv.org/pdf/2608.29917
ðProject https://lnkd.in/eNEUB_nV
ðRepo https://lnkd.in/eRyDY6Ue
ðĨ10âĪ4ð1
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ðVision Weight Estimationð
ðDoppio is a novel video dataset capturing video of falling ground coffee, paired with precise, per-frame ground-truth weight measurements: OCR readings are extracted from the display, smoothed and time-lag compensated, and paired with per-frame weight annotations. Repo to be released under Apacheð
ðReview https://www.linkedin.com/posts/visionarynet_computer-vision-weight-estimation-activity-7501900695457951744-ArBO
ðPaper https://lnkd.in/eHuy87SX
ðProject https://lnkd.in/e9g9zeK3
ðRepo https://lnkd.in/emUePTiq
ðDoppio is a novel video dataset capturing video of falling ground coffee, paired with precise, per-frame ground-truth weight measurements: OCR readings are extracted from the display, smoothed and time-lag compensated, and paired with per-frame weight annotations. Repo to be released under Apacheð
ðReview https://www.linkedin.com/posts/visionarynet_computer-vision-weight-estimation-activity-7501900695457951744-ArBO
ðPaper https://lnkd.in/eHuy87SX
ðProject https://lnkd.in/e9g9zeK3
ðRepo https://lnkd.in/emUePTiq
ðĨ10âĪ5ðū2ð1
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ðŠĢWeather-Conditioned Depth AnythingðŠĢ
ðWeather-Conditioned Depth Anything from Texas A&M is the new SOTA in weather-robust depth estimation. A curated mix of real and synthetic degradation datasets to extract content-independent, degradation-aware weather embeddings. Repo under Apacheð
ðReview https://lnkd.in/p/eW-dsepD
ðPaper https://lnkd.in/er_MvVft
ðProject https://lnkd.in/ehXPs3C7
ðRepo https://lnkd.in/edk7Ts_r
ðWeather-Conditioned Depth Anything from Texas A&M is the new SOTA in weather-robust depth estimation. A curated mix of real and synthetic degradation datasets to extract content-independent, degradation-aware weather embeddings. Repo under Apacheð
ðReview https://lnkd.in/p/eW-dsepD
ðPaper https://lnkd.in/er_MvVft
ðProject https://lnkd.in/ehXPs3C7
ðRepo https://lnkd.in/edk7Ts_r
ð2ðĨ2âĪ1
ðĨ2ðĪŊ1
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ðŧEmerging Objs from Motionðŧ
ðMotion boundaries provide a strong signal for object-level grouping and can be used to derive pseudo-instance supervision. Suitable for: mono-depth, 3D object detection, 3D occupancy, and end-to-end planning. Repo under Apache 2.0ð
ðReview https://lnkd.in/p/eezZrSJE
ðPaper https://arxiv.org/pdf/2609.04348
ðProject https://tj12342.github.io/object-concepts-from-motion/
ðRepo https://github.com/TJ12342/object-concepts-from-motion/tree/main
ðMotion boundaries provide a strong signal for object-level grouping and can be used to derive pseudo-instance supervision. Suitable for: mono-depth, 3D object detection, 3D occupancy, and end-to-end planning. Repo under Apache 2.0ð
ðReview https://lnkd.in/p/eezZrSJE
ðPaper https://arxiv.org/pdf/2609.04348
ðProject https://tj12342.github.io/object-concepts-from-motion/
ðRepo https://github.com/TJ12342/object-concepts-from-motion/tree/main
âĪ2ð2ð1
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ðĨ#AIwithPapers: we are 17,000+ðĨ
ð Even though 100+ bots are trying to join the discussion chats every day, there are 17,000 of us! Almost all of us are still humans ð§
ð Invite -> https://xn--r1a.website/AI_DeepLearning
ð Even though 100+ bots are trying to join the discussion chats every day, there are 17,000 of us! Almost all of us are still humans ð§
ð Invite -> https://xn--r1a.website/AI_DeepLearning
âĪ25ðū15ð4
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ðMcByte++ tracking-by-detectionð
ðMcByte++ is the newer extension of McByte that advances training-free sports MOT toward long-term ID tracking, while simultaneously improving efficiency and runtime performance. Repo under Apache 2.0ð
ðReview https://lnkd.in/p/e4-diVJS
ðPaper https://lnkd.in/e_Vxky-b
ðRepo https://lnkd.in/e8SeCYmk
ðMcByte++ is the newer extension of McByte that advances training-free sports MOT toward long-term ID tracking, while simultaneously improving efficiency and runtime performance. Repo under Apache 2.0ð
ðReview https://lnkd.in/p/e4-diVJS
ðPaper https://lnkd.in/e_Vxky-b
ðRepo https://lnkd.in/e8SeCYmk
âĪ8ð2ðĨ1ðĐ1ðū1
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ðĨðĨ Marigold V2 is out ðĨðĨ
ðMarigold V2 is out: depth, (impressive) see-through depth, surface normals, albedo, and other dense modalities. SOTA results. Repo under Apache 2.0ð
#AI #deeplearning #AIwithPapers
ðReview https://lnkd.in/p/eKM44yDQ
ðPaper https://arxiv.org/pdf/2609.08084
ðRepo https://github.com/huawei-bayerlab/marigold-v2
ðProject https://huggingface.co/spaces/huawei-bayerlab/marigold-v2-web
ðMarigold V2 is out: depth, (impressive) see-through depth, surface normals, albedo, and other dense modalities. SOTA results. Repo under Apache 2.0ð
#AI #deeplearning #AIwithPapers
ðReview https://lnkd.in/p/eKM44yDQ
ðPaper https://arxiv.org/pdf/2609.08084
ðRepo https://github.com/huawei-bayerlab/marigold-v2
ðProject https://huggingface.co/spaces/huawei-bayerlab/marigold-v2-web
ðĨ9âĪ3ð3ð1
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ðĶšEfficient/Scalable Video PretrainingðĶš
ðLeVJEPA1 (Yann Lecun) is the first video encoder trained under LeJEPAâs collapse-free objective, and evaluate it under frozen probing against video and image pretraining baselines retrained on identical data, in both epoch-matched and FLOP-matched regimes. Repo under MITð
ðReview https://lnkd.in/p/eJQAm3AN
ðPaper https://lnkd.in/eCzzTiNH
ðProject https://levjepa.github.io/
ðRepo https://lnkd.in/etiF5CDj
ðLeVJEPA1 (Yann Lecun) is the first video encoder trained under LeJEPAâs collapse-free objective, and evaluate it under frozen probing against video and image pretraining baselines retrained on identical data, in both epoch-matched and FLOP-matched regimes. Repo under MITð
ðReview https://lnkd.in/p/eJQAm3AN
ðPaper https://lnkd.in/eCzzTiNH
ðProject https://levjepa.github.io/
ðRepo https://lnkd.in/etiF5CDj
âĪ8ðĨ6ð1
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ðĨ RelateAnything is gold! ðĨ
ðRelateAnything 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 2.0ð
ðReview https://lnkd.in/p/etAcdFM3
ðPaper https://arxiv.org/pdf/2609.12552
ðRepo https://github.com/Maelic/RelateAnything
ðProject https://maelic.github.io/RelateAnythingProject/
ðRelateAnything 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 2.0ð
ðReview https://lnkd.in/p/etAcdFM3
ðPaper https://arxiv.org/pdf/2609.12552
ðRepo https://github.com/Maelic/RelateAnything
ðProject https://maelic.github.io/RelateAnythingProject/
âĪ12ðĨ4ð3ð1ðĪŊ1
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ð EventEgoHands++ is out! ð
ðEventEgoHands++ is a novel framework for event-based 3D hand mesh reconstruction from an egocentric viewpoint. 1M+ samples dataset! Code/Data releasedð
ðReview https://lnkd.in/p/eTbPvXbW
ðPaper https://arxiv.org/pdf/2609.17189
ðRepo https://github.com/ryhara/EventEgoHandsV2
ðProject https://ryhara.github.io/EventEgoHandsV2/
ðEventEgoHands++ is a novel framework for event-based 3D hand mesh reconstruction from an egocentric viewpoint. 1M+ samples dataset! Code/Data releasedð
ðReview https://lnkd.in/p/eTbPvXbW
ðPaper https://arxiv.org/pdf/2609.17189
ðRepo https://github.com/ryhara/EventEgoHandsV2
ðProject https://ryhara.github.io/EventEgoHandsV2/
âĪ3ðĨ2ð1
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ðĶSOTA Splashing LiquidsðĶ
ðSplashSplat reconstructs splashing liquids from real multi-view vide. Impose physical structure only where the observations can constrain it. Impressive results, SOTA. Code TBR under MITð
ðReview https://lnkd.in/p/ejMTHcp7
ðPaper https://arxiv.org/pdf/2609.20818
ðProject niko-creater.github.io/splashsplat-web/
ðRepo https://github.com/Niko-creater/Splashsplat
ðSplashSplat reconstructs splashing liquids from real multi-view vide. Impose physical structure only where the observations can constrain it. Impressive results, SOTA. Code TBR under MITð
ðReview https://lnkd.in/p/ejMTHcp7
ðPaper https://arxiv.org/pdf/2609.20818
ðProject niko-creater.github.io/splashsplat-web/
ðRepo https://github.com/Niko-creater/Splashsplat
ð4âĪ3ðĨ2ð1
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ðĨAgentic Image-to-SceneðĨ
ðHARMONY by UPenn is a hierarchical chain-of-thought framework that leverages both agentic reasoning and visual geometry foundation. Impressive 3D scenes. Repo TBAð
ðReview https://lnkd.in/p/ep2hmRSp
ðPaper https://arxiv.org/pdf/2609.26793
ðProject https://cwchenwang.github.io/harmony/
ðData https://huggingface.co/datasets/ShufanSun/harmony
ðHARMONY by UPenn is a hierarchical chain-of-thought framework that leverages both agentic reasoning and visual geometry foundation. Impressive 3D scenes. Repo TBAð
ðReview https://lnkd.in/p/ep2hmRSp
ðPaper https://arxiv.org/pdf/2609.26793
ðProject https://cwchenwang.github.io/harmony/
ðData https://huggingface.co/datasets/ShufanSun/harmony
ðĨ8âĪ2ð1
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ðŋPanoSeg3R: SOTA 3D Segmentationðŋ
ðPanoSeg3R is a novel feed-forward framework for 3D panoramic semantic segmentation. New SOTA. Code comingð
ðReview https://lnkd.in/p/eKCKWv3g
ðPaper https://arxiv.org/pdf/2609.22687
ðProject https://harryyoon777.github.io/PanoSeg3R/#
ðRepo TBA
ðPanoSeg3R is a novel feed-forward framework for 3D panoramic semantic segmentation. New SOTA. Code comingð
ðReview https://lnkd.in/p/eKCKWv3g
ðPaper https://arxiv.org/pdf/2609.22687
ðProject https://harryyoon777.github.io/PanoSeg3R/#
ðRepo TBA
âĪ5ð1ðĨ1ð1
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ðĐŧUniversal X-ray SegmentationðĐŧ
ðFleXray: universal anatomical segmentation across the entire body in clinical X-rays. Built on a scalable, physics-based generative X-ray data engine. Repo under MITð
ðReview https://lnkd.in/p/e9MUk_eq
ðPaper https://arxiv.org/pdf/2609.26756
ðProject https://flexray.csail.mit.edu/
ðRepo https://github.com/VictorButoi/FleXray
ðFleXray: universal anatomical segmentation across the entire body in clinical X-rays. Built on a scalable, physics-based generative X-ray data engine. Repo under MITð
ðReview https://lnkd.in/p/e9MUk_eq
ðPaper https://arxiv.org/pdf/2609.26756
ðProject https://flexray.csail.mit.edu/
ðRepo https://github.com/VictorButoi/FleXray
ð5âĪ3ðĨ3ð2ðĪŊ1
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ðĶī3D Foundational RadiologyðĶī
ðnnFoundation: 3D radiological foundation models designed for transferable representation learning across heterogeneous tasks/datasets. Models releasedð
ðReview https://lnkd.in/p/eNajRGBi
ðPaper https://arxiv.org/pdf/2609.26924
ðModels https://huggingface.co/collections/MIC-DKFZ/nnfoundation
ðnnFoundation: 3D radiological foundation models designed for transferable representation learning across heterogeneous tasks/datasets. Models releasedð
ðReview https://lnkd.in/p/eNajRGBi
ðPaper https://arxiv.org/pdf/2609.26924
ðModels https://huggingface.co/collections/MIC-DKFZ/nnfoundation
âĪ9ð2ð2ðĨ1ðĪĐ1
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ðĨTrackEverything is outðĨ
ðTrackEverything is the first 3D point tracker capable of tracking all visible points across long horizons (1000+ frames). Repo announcedð
ðReview https://lnkd.in/p/eCPJ6h2B
ðPaper https://arxiv.org/pdf/2609.30222
ðProject https://trackeverything.github.io/
ðRepo https://github.com/ayushjain1144/trackeverything
ðTrackEverything is the first 3D point tracker capable of tracking all visible points across long horizons (1000+ frames). Repo announcedð
ðReview https://lnkd.in/p/eCPJ6h2B
ðPaper https://arxiv.org/pdf/2609.30222
ðProject https://trackeverything.github.io/
ðRepo https://github.com/ayushjain1144/trackeverything
âĪ7ðĨ6ð2ð1