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š„CutLER: Unsupervised Segmentation š„
šNovel paper by #META on detection & instance segmentation without human annotations
šReview https://bit.ly/3DlFiUG
šPaper arxiv.org/pdf/2301.11320.pdf
šCode github.com/facebookresearch/CutLER
šProject people.eecs.berkeley.edu/~xdwang/projects/CutLER
šNovel paper by #META on detection & instance segmentation without human annotations
šReview https://bit.ly/3DlFiUG
šPaper arxiv.org/pdf/2301.11320.pdf
šCode github.com/facebookresearch/CutLER
šProject people.eecs.berkeley.edu/~xdwang/projects/CutLER
ā¤10š4š„4š¤Æ1
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š CLIP/GPT3-driven Affective Faces š
šColumbia unveils a neural framework for facial expressions retrieval given the context of the speaker
šReview https://bit.ly/3HERna0
šPaper arxiv.org/pdf/2301.10939.pdf
šProject realtalk.cs.columbia.edu
šCode github.com/scottgeng00/realtalk
šColumbia unveils a neural framework for facial expressions retrieval given the context of the speaker
šReview https://bit.ly/3HERna0
šPaper arxiv.org/pdf/2301.10939.pdf
šProject realtalk.cs.columbia.edu
šCode github.com/scottgeng00/realtalk
š„12ā¤5š1š„°1š¤©1
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š¦ Physics-inspired Computer Vision š¦
šUCLA unveils PhyCV, the first Physics-inspired Computer Vision Library
šReview https://bit.ly/3HEWozI
šCode github.com/JalaliLabUCLA/phycv
šProject photonics.ucla.edu/2022/05/12/jalali-lab-open-sources-phycv-a-physics-inspired-computer-vision-library/
šUCLA unveils PhyCV, the first Physics-inspired Computer Vision Library
šReview https://bit.ly/3HEWozI
šCode github.com/JalaliLabUCLA/phycv
šProject photonics.ucla.edu/2022/05/12/jalali-lab-open-sources-phycv-a-physics-inspired-computer-vision-library/
š¤Æ7ā¤5š4š±1
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š·Audio-Visual Semantic Segmentationš·
šA novel problem in #AI: pixel-level segmentation of objects that produce sound in the image frame
šReview https://bit.ly/3wFY6dw
šPaper arxiv.org/pdf/2301.13190.pdf
šProject opennlplab.github.io/AVSBench
šCode github.com/OpenNLPLab/AVSBench
šA novel problem in #AI: pixel-level segmentation of objects that produce sound in the image frame
šReview https://bit.ly/3wFY6dw
šPaper arxiv.org/pdf/2301.13190.pdf
šProject opennlplab.github.io/AVSBench
šCode github.com/OpenNLPLab/AVSBench
š¤Æ10š3š„2ā¤1š±1
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š Text-driven Video Neural Editing š
šA novel text-guided video editing with both appearance/shape
šReview https://bit.ly/3YcfMJO
šPaper arxiv.org/pdf/2301.13173.pdf
šProject text-video-edit.github.io/
šA novel text-guided video editing with both appearance/shape
šReview https://bit.ly/3YcfMJO
šPaper arxiv.org/pdf/2301.13173.pdf
šProject text-video-edit.github.io/
š„12š1
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ā Mono-STAR: Unified Track/3D ā
šReal-time 3D unified framework for semantic fusion, tracking, non-rigid deformation, and topological changes
šReview https://bit.ly/3Dxvxmx
šPaper arxiv.org/pdf/2301.13244.pdf
šProject github.com/changhaonan/Mono-STAR-demo
šReal-time 3D unified framework for semantic fusion, tracking, non-rigid deformation, and topological changes
šReview https://bit.ly/3Dxvxmx
šPaper arxiv.org/pdf/2301.13244.pdf
šProject github.com/changhaonan/Mono-STAR-demo
ā”5š4š„4ā¤1
šļøšļø 100% Accurated #3D Labeling šļøšļø
š#Amazon unveils a novel tool for fine-grained 3D part labeling. Up to 100% accuracy! Paper onlyš¢
šReview https://bit.ly/3kYpQHQ
šPaper https://arxiv.org/pdf/2301.10460.pdf
š#Amazon unveils a novel tool for fine-grained 3D part labeling. Up to 100% accuracy! Paper onlyš¢
šReview https://bit.ly/3kYpQHQ
šPaper https://arxiv.org/pdf/2301.10460.pdf
š¤Æ10ā¤2š1
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š§FLOW360: 360° Neural Optical Flowš§
š The first perceptually realistic 360° video benchmark dataset + SLOF method for OF tracking
šReview https://bit.ly/3wMZZoX
šPaper arxiv.org/pdf/2301.11880.pdf
šProject https://siamlof.github.io
š The first perceptually realistic 360° video benchmark dataset + SLOF method for OF tracking
šReview https://bit.ly/3wMZZoX
šPaper arxiv.org/pdf/2301.11880.pdf
šProject https://siamlof.github.io
š7š¤Æ2š„1
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šDREAMIX:General Diffusive Video Editorš
š#Google unveils the first diffusion-based method able to perform text-based motion/appearance editing of general videos
šReview https://bit.ly/3I3Hq6B
šPaper arxiv.org/pdf/2302.01329.pdf
šProject dreamix-video-editing.github.io/
š#Google unveils the first diffusion-based method able to perform text-based motion/appearance editing of general videos
šReview https://bit.ly/3I3Hq6B
šPaper arxiv.org/pdf/2302.01329.pdf
šProject dreamix-video-editing.github.io/
š¤Æ24š±3š2ā¤1
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š§© Text-Guided #3D Texturing š§©
š Text-Guided HQ textures via iterative diffusion-based process
šReview https://bit.ly/3ldC6Ez
šProject texturepaper.github.io/TEXTurePaper
šCode github.com/TEXTurePaper/TEXTurePaper
šPaper texturepaper.github.io/TEXTurePaper/static/paper.pdf
š Text-Guided HQ textures via iterative diffusion-based process
šReview https://bit.ly/3ldC6Ez
šProject texturepaper.github.io/TEXTurePaper
šCode github.com/TEXTurePaper/TEXTurePaper
šPaper texturepaper.github.io/TEXTurePaper/static/paper.pdf
š„8š¤Æ2š1
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š¦ MOSE: coMplex video Object SEgmentation š¦
šNovel Dataset for VOS is out! SOTA method on DAVIS is only 59.4% on MOSE
šReview https://bit.ly/40yzSzW
šPaper arxiv.org/pdf/2302.01872.pdf
šProject henghuiding.github.io/MOSE/
šCode github.com/henghuiding/MOSE-api
šNovel Dataset for VOS is out! SOTA method on DAVIS is only 59.4% on MOSE
šReview https://bit.ly/40yzSzW
šPaper arxiv.org/pdf/2302.01872.pdf
šProject henghuiding.github.io/MOSE/
šCode github.com/henghuiding/MOSE-api
ā¤7š2š„2
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š Gen-1: next-gen Generative #AI š
š#Runway unveils Gen-1: the next step forward for Generative AI. Registration available for beta -> hurry up!
šReview https://bit.ly/3YqQYh8
šPaper arxiv.org/pdf/2302.03011.pdf
šProject https://research.runwayml.com/gen1
š#Runway unveils Gen-1: the next step forward for Generative AI. Registration available for beta -> hurry up!
šReview https://bit.ly/3YqQYh8
šPaper arxiv.org/pdf/2302.03011.pdf
šProject https://research.runwayml.com/gen1
š¤Æ10š±3ā¤1š1š„1š¤©1
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šæDirectMHP: Multi-Head Pose Estimationšæ
šNovel E2E multi-person head pose estimation (MPHPE) under full-range angles
šReview https://bit.ly/3HJubXg
šPaper arxiv.org/pdf/2302.01110.pdf
šCode github.com/hnuzhy/DirectMHP
šNovel E2E multi-person head pose estimation (MPHPE) under full-range angles
šReview https://bit.ly/3HJubXg
šPaper arxiv.org/pdf/2302.01110.pdf
šCode github.com/hnuzhy/DirectMHP
š„13š1
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š§± LEGO-Net: Objects in Rooms š§±
šTransformer-based iterative method for rearrangement of objects in messy rooms
šReview https://bit.ly/3HR0fs6
šPaper arxiv.org/pdf/2301.09629.pdf
šProject ivl.cs.brown.edu/#/projects/lego-net
šTransformer-based iterative method for rearrangement of objects in messy rooms
šReview https://bit.ly/3HR0fs6
šPaper arxiv.org/pdf/2301.09629.pdf
šProject ivl.cs.brown.edu/#/projects/lego-net
š„11š¤Æ4
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š In-N-Out: 3D-aware OOD video editing š
šNovel 3D-aware video editing able to manipulate OOD objects (e.g. heavy makeup, accessories)
šReview https://bit.ly/3jN0CMu
šPaper arxiv.org/pdf/2302.04871.pdf
šProject https://in-n-out-3d.github.io
šNovel 3D-aware video editing able to manipulate OOD objects (e.g. heavy makeup, accessories)
šReview https://bit.ly/3jN0CMu
šPaper arxiv.org/pdf/2302.04871.pdf
šProject https://in-n-out-3d.github.io
š„4ā¤2š¤Æ2š1
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š„ø MEGANE: Generative Morphable Eyeglass š„ø
š#META unveils the most advanced #3D compositional morphable AI for eyeglasses (HD geometry/photometric interaction)
šReview https://bit.ly/3jOWifu
šPaper arxiv.org/pdf/2302.04868.pdf
šProject junxuan-li.github.io/megane
š#META unveils the most advanced #3D compositional morphable AI for eyeglasses (HD geometry/photometric interaction)
šReview https://bit.ly/3jOWifu
šPaper arxiv.org/pdf/2302.04868.pdf
šProject junxuan-li.github.io/megane
š„9š¤Æ3š2š¤©1
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š 3D-aware Blending with NeRF š
šNovel 3D-aware blending method via generative NeRFs
šReview https://bit.ly/3lBEJA2
šPaper arxiv.org/pdf/2302.06608.pdf
šProject blandocs.github.io/blendnerf
šCode github.com/naver-ai/BlendNeRF
šNovel 3D-aware blending method via generative NeRFs
šReview https://bit.ly/3lBEJA2
šPaper arxiv.org/pdf/2302.06608.pdf
šProject blandocs.github.io/blendnerf
šCode github.com/naver-ai/BlendNeRF
ā¤8
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š
Semantics-guided natural synthesis š
šAlibaba #AI unveils a novel semantics-guided synthesis of natural scenes
šReview https://bit.ly/4115MVJ
šPaper arxiv.org/pdf/2302.07224.pdf
šProject zju3dv.github.io/paintingnature
šAlibaba #AI unveils a novel semantics-guided synthesis of natural scenes
šReview https://bit.ly/4115MVJ
šPaper arxiv.org/pdf/2302.07224.pdf
šProject zju3dv.github.io/paintingnature
š5š„1š¤Æ1
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š¦ SOTA ALERT: YOWOv2 is out! š¦
š The 2nd-gen of YOWO, real-time detection of spatio-temporal actions
šReview https://bit.ly/3IscY60
šPaper arxiv.org/pdf/2302.06848v1.pdf
šCode github.com/yjh0410/YOWOv2
š The 2nd-gen of YOWO, real-time detection of spatio-temporal actions
šReview https://bit.ly/3IscY60
šPaper arxiv.org/pdf/2302.06848v1.pdf
šCode github.com/yjh0410/YOWOv2
š„17š2
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š¬ DIVOTrack: crossview MOT dataset š¬
š DIVOTrack + CrossMOT: the ultimate solution for MOT in realistic scenario
šReview https://bit.ly/3YSFZgL
šPaper arxiv.org/pdf/2302.07676.pdf
šCode github.com/shengyuhao/DIVOTrack
š DIVOTrack + CrossMOT: the ultimate solution for MOT in realistic scenario
šReview https://bit.ly/3YSFZgL
šPaper arxiv.org/pdf/2302.07676.pdf
šCode github.com/shengyuhao/DIVOTrack
š„6š2š¤Æ1