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ðĶ Instance-Level Semantics of Cells ðĶ
ðTYC: novel dataset for understanding instance-level semantics & motions of cells in microstructures
ðReview https://t.ly/y-4VZ
ðPaper arxiv.org/pdf/2308.12116.pdf
ðProject christophreich1996.github.io/tyc_dataset/
ðCode github.com/ChristophReich1996/TYC-Dataset
ðData tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3930
ðTYC: novel dataset for understanding instance-level semantics & motions of cells in microstructures
ðReview https://t.ly/y-4VZ
ðPaper arxiv.org/pdf/2308.12116.pdf
ðProject christophreich1996.github.io/tyc_dataset/
ðCode github.com/ChristophReich1996/TYC-Dataset
ðData tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/3930
ð8ðĨ3âĪ1âĄ1ðĪŊ1
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ðĩPOCO: 3D HPS + Confidenceðĩ
ð Novel framework for HPS: #3D human body + confidence in a single feed-forward pass
ðReview https://t.ly/cDePe
ðPaper arxiv.org/pdf/2308.12965.pdf
ðProject https://poco.is.tue.mpg.de
ð Novel framework for HPS: #3D human body + confidence in a single feed-forward pass
ðReview https://t.ly/cDePe
ðPaper arxiv.org/pdf/2308.12965.pdf
ðProject https://poco.is.tue.mpg.de
ðĨ5ð3âĪ2ðĪŊ1ðą1
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ð NeO360: NeRF for Sparse Outdoor ð
ð#Toyota (+GIT) unveils NeO360: 360âĶ outdoor scenes from a single or a few posed RGB images
ðReview https://t.ly/JDJZg
ðPaper arxiv.org/pdf/2308.12967.pdf
ðProject zubair-irshad.github.io/projects/neo360.html
ð#Toyota (+GIT) unveils NeO360: 360âĶ outdoor scenes from a single or a few posed RGB images
ðReview https://t.ly/JDJZg
ðPaper arxiv.org/pdf/2308.12967.pdf
ðProject zubair-irshad.github.io/projects/neo360.html
âĪ13ð3ðĨ2ðĨ°1ðĪŊ1
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ðĨ Scenimefy: I-2-I for anime ðĨ
ðS-Lab unveils a novel semi-supervised I-2-I translation framework + HD dataset for anime
ðReview https://t.ly/IsdEG
ðPaper arxiv.org/pdf/2308.12968.pdf
ðCode https://github.com/Yuxinn-J/Scenimefy
ðProject https://yuxinn-j.github.io/projects/Scenimefy.html
ðS-Lab unveils a novel semi-supervised I-2-I translation framework + HD dataset for anime
ðReview https://t.ly/IsdEG
ðPaper arxiv.org/pdf/2308.12968.pdf
ðCode https://github.com/Yuxinn-J/Scenimefy
ðProject https://yuxinn-j.github.io/projects/Scenimefy.html
ðĨ°13âĪ2ðĨ1ðū1
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ðĻ Watch Your Steps: Editing by Text ðĻ
ðThe novel SOTA in image & scene (text) editing via denoising diffusion models
ðReview https://t.ly/fv9wn
ðPaper arxiv.org/pdf/2308.08947.pdf
ðProject ashmrz.github.io/WatchYourSteps
ðThe novel SOTA in image & scene (text) editing via denoising diffusion models
ðReview https://t.ly/fv9wn
ðPaper arxiv.org/pdf/2308.08947.pdf
ðProject ashmrz.github.io/WatchYourSteps
âĪ4ð3ðĪŊ3ðĨ1
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ðĄ Relighting NeRF ðĄ
ðNeural implicit radiance representation for free viewpoint relighting of an object lit by a moving point light
ðReview https://t.ly/J-3_L
ðProject nrhints.github.io
ðCode github.com/iamNCJ/NRHints
ðPaper nrhints.github.io/pdfs/nrhints-sig23.pdf
ðNeural implicit radiance representation for free viewpoint relighting of an object lit by a moving point light
ðReview https://t.ly/J-3_L
ðProject nrhints.github.io
ðCode github.com/iamNCJ/NRHints
ðPaper nrhints.github.io/pdfs/nrhints-sig23.pdf
ðĪŊ3ð2âĪ1âĄ1ðĨ1
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ðŠķ ReST: Multi-Camera MOT ðŠķ
ðNovel reconfigurable two-steps graph model for multi-camera multi object video tracking (MC-MOT)
ðReview https://t.ly/3C5tb
ðPaper arxiv.org/pdf/2308.13229.pdf
ðCode github.com/chengche6230/ReST
ðNovel reconfigurable two-steps graph model for multi-camera multi object video tracking (MC-MOT)
ðReview https://t.ly/3C5tb
ðPaper arxiv.org/pdf/2308.13229.pdf
ðCode github.com/chengche6230/ReST
ðĨ7âĪ3ðĪĐ2
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ðēMagicEdit: Magic Video Editðē
ðMagicEdit: explicit disentangling content, structure & motion for Hi-Fi and temporally coherent video editing
ðReport https://t.ly/tREX4
ðPaper arxiv.org/pdf/2308.14749.pdf
ðProject magic-edit.github.io
ðCode github.com/magic-research/magic-edit
ðMagicEdit: explicit disentangling content, structure & motion for Hi-Fi and temporally coherent video editing
ðReport https://t.ly/tREX4
ðPaper arxiv.org/pdf/2308.14749.pdf
ðProject magic-edit.github.io
ðCode github.com/magic-research/magic-edit
ðĨ°8âĪ4ð3ðĨ1ðą1ðĪĐ1
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âïļ VideoCutLER: Simple UVIS âïļ
ðVideoCutLER is a simple unsupervised video instance segmentation (UVIS) method without relying on optical flows
ðReview https://t.ly/PBBjG
ðPaper arxiv.org/pdf/2308.14710.pdf
ðProject people.eecs.berkeley.edu/~xdwang/projects/CutLER
ðCode github.com/facebookresearch/CutLER/tree/main/videocutler
ðVideoCutLER is a simple unsupervised video instance segmentation (UVIS) method without relying on optical flows
ðReview https://t.ly/PBBjG
ðPaper arxiv.org/pdf/2308.14710.pdf
ðProject people.eecs.berkeley.edu/~xdwang/projects/CutLER
ðCode github.com/facebookresearch/CutLER/tree/main/videocutler
ðĨ8ð3âĪ2ðĪŊ1
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ðĶ 3D Pigeons Pose & Tracking ðĶ
ð 3D-MuPPET: estimate and track 3D poses of pigeons with multiple-views
ðReview https://t.ly/jfAJJ
ðPaper arxiv.org/pdf/2308.15316.pdf
ðCode github.com/alexhang212/3D-MuPPET/
ð 3D-MuPPET: estimate and track 3D poses of pigeons with multiple-views
ðReview https://t.ly/jfAJJ
ðPaper arxiv.org/pdf/2308.15316.pdf
ðCode github.com/alexhang212/3D-MuPPET/
ðĪĢ17ðĪŊ14ð4ðĨ°2âĪ1ðĪĐ1
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ðRoboTAP: Dense Tracking for Few-Shot Imitationð
ðRoboTAP: novel dense tracking representation for robotic arm
ðReview https://t.ly/MCO_V
ðPaper arxiv.org/pdf/2308.15975.pdf
ðProject https://robotap.github.io/
ðCode github.com/deepmind/tapnet
ðRoboTAP: novel dense tracking representation for robotic arm
ðReview https://t.ly/MCO_V
ðPaper arxiv.org/pdf/2308.15975.pdf
ðProject https://robotap.github.io/
ðCode github.com/deepmind/tapnet
ðĨ8ð2ðĪŊ2ðĪĐ1
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âšFACET: Fairness in Computer Visionâš
ð#META AI opens a large, publicly available dataset for classification, detection & segmentation. Potential performance disparities & challenges across sensitive demographic attributes
ðReview https://t.ly/mKn-t
ðPaper arxiv.org/pdf/2309.00035.pdf
ðDataset https://facet.metademolab.com/
ð#META AI opens a large, publicly available dataset for classification, detection & segmentation. Potential performance disparities & challenges across sensitive demographic attributes
ðReview https://t.ly/mKn-t
ðPaper arxiv.org/pdf/2309.00035.pdf
ðDataset https://facet.metademolab.com/
ðĨ10âĪ6ð4ð1
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âïļ Doppelgangers in Structures âïļ
ðA novel learning-based approach for visual disambiguation: distinguishing illusory matches to produce correct, disambiguated #3D reconstructions
ðReview https://t.ly/9yLot
ðPaper arxiv.org/pdf/2309.02420.pdf
ðCode github.com/RuojinCai/Doppelgangers
ðProject doppelgangers-3d.github.io/
ðA novel learning-based approach for visual disambiguation: distinguishing illusory matches to produce correct, disambiguated #3D reconstructions
ðReview https://t.ly/9yLot
ðPaper arxiv.org/pdf/2309.02420.pdf
ðCode github.com/RuojinCai/Doppelgangers
ðProject doppelgangers-3d.github.io/
ðĨ8ð3ðĪŊ2ð1
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ð Tracking Anything with Decoupled VOS ð
ðA novel VOS approach that extends SAM for open-world video segmentation with no user input required
ðReview https://t.ly/xeobR
ðPaper arxiv.org/pdf/2309.03903.pdf
ðProject hkchengrex.com/Tracking-Anything-with-DEVA
ðCode github.com/hkchengrex/Tracking-Anything-with-DEVA
ðColab https://colab.research.google.com/drive/1OsyNVoV_7ETD1zIE8UWxL3NXxu12m_YZ
ðA novel VOS approach that extends SAM for open-world video segmentation with no user input required
ðReview https://t.ly/xeobR
ðPaper arxiv.org/pdf/2309.03903.pdf
ðProject hkchengrex.com/Tracking-Anything-with-DEVA
ðCode github.com/hkchengrex/Tracking-Anything-with-DEVA
ðColab https://colab.research.google.com/drive/1OsyNVoV_7ETD1zIE8UWxL3NXxu12m_YZ
ðĨ13ð6ðĪŊ4âĪ2ðĒ1ðĪĐ1
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ðŠ· Diffusive Consistent Video Editing ðŠ·
ð Weizmann Institute of Science unveils TokenFlow, a novel text-to-image diffusion model for text-driven video editing
ðReview https://t.ly/ru8km
ðPaper arxiv.org/pdf/2307.10373.pdf
ðProject diffusion-tokenflow.github.io
ðCode github.com/omerbt/TokenFlow
ð Weizmann Institute of Science unveils TokenFlow, a novel text-to-image diffusion model for text-driven video editing
ðReview https://t.ly/ru8km
ðPaper arxiv.org/pdf/2307.10373.pdf
ðProject diffusion-tokenflow.github.io
ðCode github.com/omerbt/TokenFlow
âĪ9ð6ðĨ2ðĪŊ1ðą1ðĒ1
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ðĨðĨ #META's DINOv2 is now commercial! ðĨðĨ
ðUniversal features for image classification, instance retrieval, video understanding, depth & semantic segmentation. Now suitable for commercial.
ðReview https://t.ly/LNrGy
ðPaper arxiv.org/pdf/2304.07193.pdf
ðCode github.com/facebookresearch/dinov2
ðDemo dinov2.metademolab.com/
ðUniversal features for image classification, instance retrieval, video understanding, depth & semantic segmentation. Now suitable for commercial.
ðReview https://t.ly/LNrGy
ðPaper arxiv.org/pdf/2304.07193.pdf
ðCode github.com/facebookresearch/dinov2
ðDemo dinov2.metademolab.com/
ðĨ15ð3âĪ1ðĪŊ1ðą1
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ð§FreeMan: towards #3D Humans ð§
ðFreeMan: the first large-scale, real-world, multi-view dataset for #3D human pose estimation. 11M frames!
ðReview https://t.ly/ICxpA
ðPaper arxiv.org/pdf/2309.05073.pdf
ðProject wangjiongw.github.io/freeman
ðFreeMan: the first large-scale, real-world, multi-view dataset for #3D human pose estimation. 11M frames!
ðReview https://t.ly/ICxpA
ðPaper arxiv.org/pdf/2309.05073.pdf
ðProject wangjiongw.github.io/freeman
ð6ðĪŊ4ðĨ°1
ðĶ MagiCapture: HD Multi-Concept Portrait ðĶ
ðKAIST unveils MagiCapture: integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references
ðReview https://t.ly/c9rOo
ðPaper https://arxiv.org/pdf/2309.06895.pdf
ðKAIST unveils MagiCapture: integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references
ðReview https://t.ly/c9rOo
ðPaper https://arxiv.org/pdf/2309.06895.pdf
âĪ5ðĨ°1
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â― Dynamic NeRFs for Soccer â―
ðSoccerNeRF: first attempt of "cheap" NeRF applied to football for reconstructing soccer replays in space and time.
ðReview https://t.ly/Ywcvk
ðPaper arxiv.org/pdf/2309.06802.pdf
ðProject https://soccernerfs.isach.be/
ðCode github.com/iSach/SoccerNeRFs
ðSoccerNeRF: first attempt of "cheap" NeRF applied to football for reconstructing soccer replays in space and time.
ðReview https://t.ly/Ywcvk
ðPaper arxiv.org/pdf/2309.06802.pdf
ðProject https://soccernerfs.isach.be/
ðCode github.com/iSach/SoccerNeRFs
ðĨ8âĪ4ð3ðĪĐ2ðĨ°1
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âĒïļ GlueStick: Graph Neural Matching âĒïļ
ðGlueStick is joint deep matcher for points and lines that leverages the connectivity information between nodes to better glue them together
ðReview https://t.ly/Atxqo
ðPaper arxiv.org/pdf/2304.02008.pdf
ðCode https://github.com/cvg/GlueStick
ðGlueStick is joint deep matcher for points and lines that leverages the connectivity information between nodes to better glue them together
ðReview https://t.ly/Atxqo
ðPaper arxiv.org/pdf/2304.02008.pdf
ðCode https://github.com/cvg/GlueStick
ðĨ11ð4âĪ1ðĪŊ1ðĪĐ1