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☢️ GlueStick: Graph Neural Matching ☢️
👉GlueStick is joint deep matcher for points and lines that leverages the connectivity information between nodes to better glue them together
😎Review https://t.ly/Atxqo
😎Paper arxiv.org/pdf/2304.02008.pdf
😎Code https://github.com/cvg/GlueStick
👉GlueStick is joint deep matcher for points and lines that leverages the connectivity information between nodes to better glue them together
😎Review https://t.ly/Atxqo
😎Paper arxiv.org/pdf/2304.02008.pdf
😎Code https://github.com/cvg/GlueStick
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🫀CPR-Coach: Neural Cardiopulmonary Resuscitation🫀
👉CPR-Coach: fine-grained action recognition in cardiopulmonary resuscitation
😎Review https://t.ly/Qbg4K
😎Paper arxiv.org/pdf/2309.11718.pdf
😎Code github.com/Shunli-Wang/CPR-Coach
😎Project shunli-wang.github.io/CPR-Coach
👉CPR-Coach: fine-grained action recognition in cardiopulmonary resuscitation
😎Review https://t.ly/Qbg4K
😎Paper arxiv.org/pdf/2309.11718.pdf
😎Code github.com/Shunli-Wang/CPR-Coach
😎Project shunli-wang.github.io/CPR-Coach
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🧪 NeuralLabeling with NeRF 🧪
👉Annotating a scene by generating segmentation masks, affordance maps, 2D bounding boxes, 3D BB, 6DOF poses, depth & meshes.
😎Review https://t.ly/1GPsj
😎Paper arxiv.org/pdf/2309.11966.pdf
😎Code github.com/FlorisE/neural-labeling
😎Project florise.github.io/neural_labeling_web
👉Annotating a scene by generating segmentation masks, affordance maps, 2D bounding boxes, 3D BB, 6DOF poses, depth & meshes.
😎Review https://t.ly/1GPsj
😎Paper arxiv.org/pdf/2309.11966.pdf
😎Code github.com/FlorisE/neural-labeling
😎Project florise.github.io/neural_labeling_web
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🍟 DE-ViT: detecting everything via DINOv2 🍟
👉DE-ViT: open-set object detector based on DINOv2 backbone. It's the new SOTA on COCO & LVIS dataset
😎Review https://t.ly/_DAmt
😎Paper arxiv.org/pdf/2309.12969.pdf
😎Code https://github.com/mlzxy/devit
👉DE-ViT: open-set object detector based on DINOv2 backbone. It's the new SOTA on COCO & LVIS dataset
😎Review https://t.ly/_DAmt
😎Paper arxiv.org/pdf/2309.12969.pdf
😎Code https://github.com/mlzxy/devit
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🛵CoTracker: fast transformer-tracker🛵
👉META's CoTracker is a fast transformer-based model that can track any point in a video
😎Review https://t.ly/M36A_
😎Paper arxiv.org/pdf/2307.07635.pdf
😎Project https://co-tracker.github.io/
😎Code github.com/facebookresearch/co-tracker
👉META's CoTracker is a fast transformer-based model that can track any point in a video
😎Review https://t.ly/M36A_
😎Paper arxiv.org/pdf/2307.07635.pdf
😎Project https://co-tracker.github.io/
😎Code github.com/facebookresearch/co-tracker
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🌬️ Neural Blowing in Still Photos 🌬️
👉 A novel approach to animate human hair (and clothes) in a still portraits
😎Review https://t.ly/HKG0t
😎Paper arxiv.org/pdf/2309.14207.pdf
😎Project nevergiveu.github.io/AutomaticHairBlowing
👉 A novel approach to animate human hair (and clothes) in a still portraits
😎Review https://t.ly/HKG0t
😎Paper arxiv.org/pdf/2309.14207.pdf
😎Project nevergiveu.github.io/AutomaticHairBlowing
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🌮 OW Indoor Segmentation 🌮
👉3D-OWIS is a novel open-world 3D indoor instance segmentation method (with auto-labeling scheme) to separate known/unknown category labels
😎Review https://t.ly/-7ALf
😎Paper arxiv.org/pdf/2309.14338.pdf
😎Code github.com/aminebdj/3D-OWIS
👉3D-OWIS is a novel open-world 3D indoor instance segmentation method (with auto-labeling scheme) to separate known/unknown category labels
😎Review https://t.ly/-7ALf
😎Paper arxiv.org/pdf/2309.14338.pdf
😎Code github.com/aminebdj/3D-OWIS
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🧱 Generating Scenes from Touch 🧱
👉#AI for synthesizing images from tactile signals (and vice versa) and apply it to a number of visuo-tactile synthesis tasks
😎Review https://t.ly/Gxr0L
😎Paper https://arxiv.org/pdf/2309.15117.pdf
😎Project https://fredfyyang.github.io/vision-from-touch
😎Code https://github.com/fredfyyang/vision-from-touch
👉#AI for synthesizing images from tactile signals (and vice versa) and apply it to a number of visuo-tactile synthesis tasks
😎Review https://t.ly/Gxr0L
😎Paper https://arxiv.org/pdf/2309.15117.pdf
😎Project https://fredfyyang.github.io/vision-from-touch
😎Code https://github.com/fredfyyang/vision-from-touch
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☕Decaf: 3D Face-Hand Interactions☕
👉The first learning-based MoCap to track human hands interacting with human faces in #3D from single monocular RGB videos
😎Review https://t.ly/070Tj
😎Paper arxiv.org/pdf/2309.16670.pdf
😎Project vcai.mpi-inf.mpg.de/projects/Decaf
👉The first learning-based MoCap to track human hands interacting with human faces in #3D from single monocular RGB videos
😎Review https://t.ly/070Tj
😎Paper arxiv.org/pdf/2309.16670.pdf
😎Project vcai.mpi-inf.mpg.de/projects/Decaf
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🌱 Making LLaMA See and Draw 🌱
👉Tencent #AI planted a SEED of Vision in Large Language Model. Making LLaMA see 'n' draw stuff.
😎Review https://t.ly/QiCAv
😎Paper arxiv.org/pdf/2310.01218.pdf
😎Code github.com/AILab-CVC/SEED
👉Tencent #AI planted a SEED of Vision in Large Language Model. Making LLaMA see 'n' draw stuff.
😎Review https://t.ly/QiCAv
😎Paper arxiv.org/pdf/2310.01218.pdf
😎Code github.com/AILab-CVC/SEED
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🔥Visual-Math Q&A: MathVista is out! 🔥
👉 MathVista is the ultimate benchmark designed to amalgamate challenges from diverse mathematical and visual tasks
😎Review https://t.ly/yfqHZ
😎Paper https://arxiv.org/pdf/2310.02255.pdf
😎Project https://mathvista.github.io/
😎Code github.com/lupantech/MathVista
👉 MathVista is the ultimate benchmark designed to amalgamate challenges from diverse mathematical and visual tasks
😎Review https://t.ly/yfqHZ
😎Paper https://arxiv.org/pdf/2310.02255.pdf
😎Project https://mathvista.github.io/
😎Code github.com/lupantech/MathVista
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💚💙 Where Is OpenCV 5? 💙💚
👉On October 24th, the organization is launching a crowdfunding campaign to raise funds for #OpenCV 5 development.
👆me in 2008 during my thesis work about face tracking; up to 50x faster than the previous SOTA. No chance to did it without OpenCV library and support from the community.
🔥Support #OpenCV 5 to create the next-gen of researchers and scientists. Spread the voice: https://t.ly/UTukV
👉On October 24th, the organization is launching a crowdfunding campaign to raise funds for #OpenCV 5 development.
👆me in 2008 during my thesis work about face tracking; up to 50x faster than the previous SOTA. No chance to did it without OpenCV library and support from the community.
🔥Support #OpenCV 5 to create the next-gen of researchers and scientists. Spread the voice: https://t.ly/UTukV
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🏊SwimXYZ: Synthetic Swim🏊
👉SwimXYZ: synthetic dataset for swimming, monocular videos annotated with ground truth 2D and 3D joints
😎Review https://t.ly/F-rdF
😎Paper arxiv.org/pdf/2310.04360.pdf
😎Data g-fiche.github.io/research-pages/swimxyz
👉SwimXYZ: synthetic dataset for swimming, monocular videos annotated with ground truth 2D and 3D joints
😎Review https://t.ly/F-rdF
😎Paper arxiv.org/pdf/2310.04360.pdf
😎Data g-fiche.github.io/research-pages/swimxyz
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📊 TextPSG: PSG from Text 📊
👉A novel problem in #AI: Panoptic Scene Graph Generation from Purely Textual Descriptions (Caption-toPSG)
😎Review https://t.ly/UXEmk
😎Paper arxiv.org/pdf/2310.07056.pdf
😎Project vis-www.cs.umass.edu/TextPSG
😎Code github.com/chengyzhao/TextPSG
👉A novel problem in #AI: Panoptic Scene Graph Generation from Purely Textual Descriptions (Caption-toPSG)
😎Review https://t.ly/UXEmk
😎Paper arxiv.org/pdf/2310.07056.pdf
😎Project vis-www.cs.umass.edu/TextPSG
😎Code github.com/chengyzhao/TextPSG
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🙋 Full Human Motion 🙋
👉OmniControl by Google is novel framework for text-conditioned human motion generation model based on diffusion process
😎Review https://t.ly/F_0Ov
😎Paper arxiv.org/pdf/2310.08580.pdf
😎Project neu-vi.github.io/omnicontrol/
👉OmniControl by Google is novel framework for text-conditioned human motion generation model based on diffusion process
😎Review https://t.ly/F_0Ov
😎Paper arxiv.org/pdf/2310.08580.pdf
😎Project neu-vi.github.io/omnicontrol/
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🦹♀️ Snap's Hyper-Realistic Human 🦹♀️
👉New diffusive #AI by Snap that generates in-the-wild human images with hyper-realism. Swipe the gallery, NUTS!👇
😎Gallery https://t.ly/cG74X
😎Paper arxiv.org/pdf/2310.08579.pdf
😎Project snap-research.github.io/HyperHuman
😎Code github.com/snap-research/HyperHuman
👉New diffusive #AI by Snap that generates in-the-wild human images with hyper-realism. Swipe the gallery, NUTS!👇
😎Gallery https://t.ly/cG74X
😎Paper arxiv.org/pdf/2310.08579.pdf
😎Project snap-research.github.io/HyperHuman
😎Code github.com/snap-research/HyperHuman
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👗AG3D clothed avatar from 2D👗
👉The novel SOTA in adversarial generative of realistic 3D people
😎Review https://t.ly/vnJO7
😎Project https://zj-dong.github.io/AG3D
😎Code https://github.com/zj-dong/AG3D
😎Paper zj-dong.github.io/AG3D/assets/paper.pdf
👉The novel SOTA in adversarial generative of realistic 3D people
😎Review https://t.ly/vnJO7
😎Project https://zj-dong.github.io/AG3D
😎Code https://github.com/zj-dong/AG3D
😎Paper zj-dong.github.io/AG3D/assets/paper.pdf
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🌱Pose-Format: All-in-One Pose🌱
👉 Pose-format: a comprehensive toolkit designed for human pose: unified, flexible, and easy-to-use
😎Review https://t.ly/rFrhq
😎Paper arxiv.org/pdf/2310.09066.pdf
😎Code github.com/sign-language-processing/pose
👉 Pose-format: a comprehensive toolkit designed for human pose: unified, flexible, and easy-to-use
😎Review https://t.ly/rFrhq
😎Paper arxiv.org/pdf/2310.09066.pdf
😎Code github.com/sign-language-processing/pose
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😻 CatFLW: Cat Neural Landmarks 😻
👉Landmark convolution neural network-based model for cat faces
😎Review https://t.ly/Y3mQ8
😎Paper arxiv.org/pdf/2305.04232.pdf
😎Dataset www.tech4animals.org/catflw
👉Landmark convolution neural network-based model for cat faces
😎Review https://t.ly/Y3mQ8
😎Paper arxiv.org/pdf/2305.04232.pdf
😎Dataset www.tech4animals.org/catflw
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🍡4K4D: Real-Time 4D at 4K🍡
👉THE new SOTA in view synthesis of dynamic 3D scenes at 4K. 30x faster, up to 400 FPS. Nuts!
😎Review https://t.ly/6ddQh
😎Paper arxiv.org/pdf/2310.11448.pdf
😎Project zju3dv.github.io/4k4d/
😎Code github.com/zju3dv/4K4D
👉THE new SOTA in view synthesis of dynamic 3D scenes at 4K. 30x faster, up to 400 FPS. Nuts!
😎Review https://t.ly/6ddQh
😎Paper arxiv.org/pdf/2310.11448.pdf
😎Project zju3dv.github.io/4k4d/
😎Code github.com/zju3dv/4K4D
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