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🥬 Rotational neural keypoint 🥬
👉RaCo is a rotationally equivariant neural keypoint detector that identifies stable and repeatable keypoints. Code under Apache 2.0💙
👉Review https://lnkd.in/p/ezQzUwju
👉Paper https://arxiv.org/pdf/2602.15755
👉Repo https://github.com/cvg/RaCo
👉RaCo is a rotationally equivariant neural keypoint detector that identifies stable and repeatable keypoints. Code under Apache 2.0💙
👉Review https://lnkd.in/p/ezQzUwju
👉Paper https://arxiv.org/pdf/2602.15755
👉Repo https://github.com/cvg/RaCo
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Hi everyone 👋
over the past months, engagement on my posts has grown a lot, which tells me the content you value is getting better. At the same time, impressions have dropped, and I think a big part of the reason is the flood of low-quality viral posts filling many of our feeds. PROVE ME WRONG 😂
The best way to push back is to engage with the posts that are actually worth your time. Open the post on LinkedIn, then leave a comment (even a couple of lines with your take), and like, share, or save it if it's useful. Comments are the most valuable, but every action helps. With ~100,000 of us following along, this is how we tell the algorithm, and me, what deserves more space.
Fight fire with fire!
over the past months, engagement on my posts has grown a lot, which tells me the content you value is getting better. At the same time, impressions have dropped, and I think a big part of the reason is the flood of low-quality viral posts filling many of our feeds. PROVE ME WRONG 😂
The best way to push back is to engage with the posts that are actually worth your time. Open the post on LinkedIn, then leave a comment (even a couple of lines with your take), and like, share, or save it if it's useful. Comments are the most valuable, but every action helps. With ~100,000 of us following along, this is how we tell the algorithm, and me, what deserves more space.
Fight fire with fire!
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🐇Segment where you look🐇
👉KAUST (+Adobe) revisited the interface through which a vision-language model passes the location of its referent to a segmentation decoder. Code announced💙
👉Review https://lnkd.in/p/er_ifxGR
👉Paper https://lnkd.in/eATXw98R
👉Project https://lnkd.in/eXSWvxPN
👉Repo TBA
👉KAUST (+Adobe) revisited the interface through which a vision-language model passes the location of its referent to a segmentation decoder. Code announced💙
👉Review https://lnkd.in/p/er_ifxGR
👉Paper https://lnkd.in/eATXw98R
👉Project https://lnkd.in/eXSWvxPN
👉Repo TBA
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🧊SOTA category-level 9D Pose🧊
👉SJTU at #NeurIPS 2026 with the new SOTA among RGB-based methods for category-level 9D pose estimation. Code announced💙
👉Review https://lnkd.in/p/e5BvM7Ua
👉Paper https://arxiv.org/pdf/2610.18374
👉Project https://souglad21.github.io/SchemaPose/
👉Repo TBA
👉SJTU at #NeurIPS 2026 with the new SOTA among RGB-based methods for category-level 9D pose estimation. Code announced💙
👉Review https://lnkd.in/p/e5BvM7Ua
👉Paper https://arxiv.org/pdf/2610.18374
👉Project https://souglad21.github.io/SchemaPose/
👉Repo TBA
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🎊 Pixel-Level 4D Humans 🎊
👉Dense4D from the Imperial College is the first feed-forward model dedicated to dense 4D correspondence across the body, clothing and hair from a set of monocular RGB images, without a body template. Code announced💙
👉Review https://lnkd.in/p/e9GXGggz
👉Paper https://arxiv.org/pdf/2610.22325
👉Project https://alpguler.com/dense4d/
👉Repo TBA
👉Dense4D from the Imperial College is the first feed-forward model dedicated to dense 4D correspondence across the body, clothing and hair from a set of monocular RGB images, without a body template. Code announced💙
👉Review https://lnkd.in/p/e9GXGggz
👉Paper https://arxiv.org/pdf/2610.22325
👉Project https://alpguler.com/dense4d/
👉Repo TBA
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🔥700 FPS Humans Recovery🔥
👉One model, any sparse signal: OmniPoser recovers full-body 3D human motion from 3D keypoints, sparse IMU rotations, head-mounted 6-DoF poses, or any subset or combination of these signals. All within a SINGLE model. Repo under nc-cc license💙
👉Review https://lnkd.in/p/et-Pk-Dy
👉Paper https://arxiv.org/pdf/2610.19857
👉Project https://jamesbridgewater51.github.io/omniposer-eccv/
👉Repo https://github.com/JamesBridgewater51/omniposer
👉One model, any sparse signal: OmniPoser recovers full-body 3D human motion from 3D keypoints, sparse IMU rotations, head-mounted 6-DoF poses, or any subset or combination of these signals. All within a SINGLE model. Repo under nc-cc license💙
👉Review https://lnkd.in/p/et-Pk-Dy
👉Paper https://arxiv.org/pdf/2610.19857
👉Project https://jamesbridgewater51.github.io/omniposer-eccv/
👉Repo https://github.com/JamesBridgewater51/omniposer
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🏵️Lang-Informed Lossless Encoder🏵️
👉Stanford's LILIE: an invertible image transform trained such that a vision-language model can recover captions from noised features. Code, encoders, and generative models💙
👉Review https://lnkd.in/p/dq3SK_nc
👉Paper https://arxiv.org/pdf/2610.23653
👉Project https://www.suchiragarwal.com/lilie/
👉Repo https://github.com/SuchirAgarwal/lilie
👉Stanford's LILIE: an invertible image transform trained such that a vision-language model can recover captions from noised features. Code, encoders, and generative models💙
👉Review https://lnkd.in/p/dq3SK_nc
👉Paper https://arxiv.org/pdf/2610.23653
👉Project https://www.suchiragarwal.com/lilie/
👉Repo https://github.com/SuchirAgarwal/lilie
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🔥SOTA Video Matting🔥
👉VideoMaMa++ (#NeurIPS 2026): mask-guided video matting framework that recovers fine-grained details (e.g., hair strands) and scales reliably to long clips. Impressive, code announced💙
👉Review https://lnkd.in/p/dj3_zE3P
👉Paper https://arxiv.org/pdf/2610.26857
👉Project https://cvlab-kaist.github.io/VideoMaMa-PlusPlus/
👉Repo https://github.com/cvlab-kaist/VideoMaMa-PlusPlus
👉VideoMaMa++ (#NeurIPS 2026): mask-guided video matting framework that recovers fine-grained details (e.g., hair strands) and scales reliably to long clips. Impressive, code announced💙
👉Review https://lnkd.in/p/dj3_zE3P
👉Paper https://arxiv.org/pdf/2610.26857
👉Project https://cvlab-kaist.github.io/VideoMaMa-PlusPlus/
👉Repo https://github.com/cvlab-kaist/VideoMaMa-PlusPlus
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🥭Agentic Manga->Animation🥭
👉Manga2Animate: turning existing manga pages into coherent animation that stays faithful to the original manga. Impressive results, best with sound. Code released.💙
👉Review https://lnkd.in/p/dHpdPAj3
👉Paper https://arxiv.org/pdf/2610.26851
👉Project https://annchenn.github.io/Manga2Animate/
👉Repo https://github.com/annchenn/Manga2Animate
👉Manga2Animate: turning existing manga pages into coherent animation that stays faithful to the original manga. Impressive results, best with sound. Code released.💙
👉Review https://lnkd.in/p/dHpdPAj3
👉Paper https://arxiv.org/pdf/2610.26851
👉Project https://annchenn.github.io/Manga2Animate/
👉Repo https://github.com/annchenn/Manga2Animate
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🍵3D from Single Human RGB🍵
👉Real-to-semantic-to-real pipeline that turns one monocular RGB video of a human demonstration into thousands of robot demonstrations. Code TBA💙
👉Review https://lnkd.in/p/dt-B2QMt
👉Paper https://arxiv.org/pdf/2610.28880
👉Project https://real2sem2real.github.io/
👉Repo TBA
👉Real-to-semantic-to-real pipeline that turns one monocular RGB video of a human demonstration into thousands of robot demonstrations. Code TBA💙
👉Review https://lnkd.in/p/dt-B2QMt
👉Paper https://arxiv.org/pdf/2610.28880
👉Project https://real2sem2real.github.io/
👉Repo TBA
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🧊 SOTA Foundation Pose 🧊
👉From one segmented RGB-D observation, directly predict 6D pose, metric size, and camera-frame completed shape. Repo under Apace 2.0💙
👉Review https://lnkd.in/p/dXpXt7Pz
👉Paper https://arxiv.org/pdf/2610.31623
👉Project https://jrryzh.github.io/F2G-Pose/
👉Repo https://github.com/jrryzh/F2G-Pose
👉From one segmented RGB-D observation, directly predict 6D pose, metric size, and camera-frame completed shape. Repo under Apace 2.0💙
👉Review https://lnkd.in/p/dXpXt7Pz
👉Paper https://arxiv.org/pdf/2610.31623
👉Project https://jrryzh.github.io/F2G-Pose/
👉Repo https://github.com/jrryzh/F2G-Pose
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🫀 SKIM V2 is out🫀
👉SOMA recovers muscle deformation over time from surface observations captured by multi-view RGB cameras, the first approach of this kind. SKIM v2: a new soft-tissue deformation dataset. Code under MIT, data & weights NC💙
👉Review https://lnkd.in/p/dB4XS7vB
👉Paper https://lnkd.in/d64S_qxj
👉Project https://lnkd.in/dWtavYGn
👉Repo https://lnkd.in/dXNJqY6N
👉SOMA recovers muscle deformation over time from surface observations captured by multi-view RGB cameras, the first approach of this kind. SKIM v2: a new soft-tissue deformation dataset. Code under MIT, data & weights NC💙
👉Review https://lnkd.in/p/dB4XS7vB
👉Paper https://lnkd.in/d64S_qxj
👉Project https://lnkd.in/dWtavYGn
👉Repo https://lnkd.in/dXNJqY6N
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