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๐ชฐ #3D Auto-Reconstruction ๐ชฐ
๐AutoRecon: automated discovery & reconstruction of objects from multi-view pics.
๐Review https://bit.ly/3MxI0f4
๐Paper arxiv.org/pdf/2305.08810.pdf
๐Project zju3dv.github.io/autorecon/
๐Code github.com/zju3dv/AutoRecon
๐AutoRecon: automated discovery & reconstruction of objects from multi-view pics.
๐Review https://bit.ly/3MxI0f4
๐Paper arxiv.org/pdf/2305.08810.pdf
๐Project zju3dv.github.io/autorecon/
๐Code github.com/zju3dv/AutoRecon
๐ฅ11โค4๐คฏ3๐ฅฐ1
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๐๏ธ Scene Five: Through Her Eyes ๐๏ธ
๐ #3D scene reconstruction of what a person is observing using only the reflections of their eyes
๐Review https://t.ly/uBO6
๐Paper arxiv.org/pdf/2306.09348.pdf
๐Project https://world-from-eyes.github.io/
๐ #3D scene reconstruction of what a person is observing using only the reflections of their eyes
๐Review https://t.ly/uBO6
๐Paper arxiv.org/pdf/2306.09348.pdf
๐Project https://world-from-eyes.github.io/
๐คฏ28๐ฅ12๐ฉ2๐คฉ1
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๐ฉโ๐ HD Avatar via Text & Pose ๐ฉโ๐
๐ Generating expressive #3D avatars from nothing but text descriptions & pose guidance
๐Review https://t.ly/wrSMH
๐Paper arxiv.org/pdf/2308.03610.pdf
๐Project avatarverse3d.github.io
๐ Generating expressive #3D avatars from nothing but text descriptions & pose guidance
๐Review https://t.ly/wrSMH
๐Paper arxiv.org/pdf/2308.03610.pdf
๐Project avatarverse3d.github.io
โค7๐ฅฐ4๐1๐คฏ1
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๐ตPOCO: 3D HPS + Confidence๐ต
๐ Novel framework for HPS: #3D human body + confidence in a single feed-forward pass
๐Review https://t.ly/cDePe
๐Paper arxiv.org/pdf/2308.12965.pdf
๐Project https://poco.is.tue.mpg.de
๐ Novel framework for HPS: #3D human body + confidence in a single feed-forward pass
๐Review https://t.ly/cDePe
๐Paper arxiv.org/pdf/2308.12965.pdf
๐Project https://poco.is.tue.mpg.de
๐ฅ5๐3โค2๐คฏ1๐ฑ1
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โ๏ธ Doppelgangers in Structures โ๏ธ
๐A novel learning-based approach for visual disambiguation: distinguishing illusory matches to produce correct, disambiguated #3D reconstructions
๐Review https://t.ly/9yLot
๐Paper arxiv.org/pdf/2309.02420.pdf
๐Code github.com/RuojinCai/Doppelgangers
๐Project doppelgangers-3d.github.io/
๐A novel learning-based approach for visual disambiguation: distinguishing illusory matches to produce correct, disambiguated #3D reconstructions
๐Review https://t.ly/9yLot
๐Paper arxiv.org/pdf/2309.02420.pdf
๐Code github.com/RuojinCai/Doppelgangers
๐Project doppelgangers-3d.github.io/
๐ฅ8๐3๐คฏ2๐1
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๐งFreeMan: towards #3D Humans ๐ง
๐FreeMan: the first large-scale, real-world, multi-view dataset for #3D human pose estimation. 11M frames!
๐Review https://t.ly/ICxpA
๐Paper arxiv.org/pdf/2309.05073.pdf
๐Project wangjiongw.github.io/freeman
๐FreeMan: the first large-scale, real-world, multi-view dataset for #3D human pose estimation. 11M frames!
๐Review https://t.ly/ICxpA
๐Paper arxiv.org/pdf/2309.05073.pdf
๐Project wangjiongw.github.io/freeman
๐6๐คฏ4๐ฅฐ1
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โDecaf: 3D Face-Hand Interactionsโ
๐The first learning-based MoCap to track human hands interacting with human faces in #3D from single monocular RGB videos
๐Review https://t.ly/070Tj
๐Paper arxiv.org/pdf/2309.16670.pdf
๐Project vcai.mpi-inf.mpg.de/projects/Decaf
๐The first learning-based MoCap to track human hands interacting with human faces in #3D from single monocular RGB videos
๐Review https://t.ly/070Tj
๐Paper arxiv.org/pdf/2309.16670.pdf
๐Project vcai.mpi-inf.mpg.de/projects/Decaf
๐8๐คฏ8๐ฅ3โค1๐1
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๐ง Depth Conditioning ๐ง
๐LooseControl to control the generative image modeling process. Layout by boundaries and #3D box control via object locations (approximate bounding boxes)
๐Review https://t.ly/9y72m
๐Paper https://arxiv.org/pdf/2312.03079.pdf
๐Project https://shariqfarooq123.github.io/loose-control/
๐Repo https://github.com/shariqfarooq123/LooseControl
๐LooseControl to control the generative image modeling process. Layout by boundaries and #3D box control via object locations (approximate bounding boxes)
๐Review https://t.ly/9y72m
๐Paper https://arxiv.org/pdf/2312.03079.pdf
๐Project https://shariqfarooq123.github.io/loose-control/
๐Repo https://github.com/shariqfarooq123/LooseControl
๐ฅ14โค6๐คฏ4๐1๐ฅฐ1
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๐ชฎHAAR: Text-Driven Generative Hairstyles๐ชฎ
๐ HAAR: new strand-based generative model for #3D human hairstyles driven by textual input.
๐Review https://t.ly/L38iD
๐Project https://haar.is.tue.mpg.de/
๐Paper https://arxiv.org/pdf/2312.11666.pdf
๐Repo coming
๐ HAAR: new strand-based generative model for #3D human hairstyles driven by textual input.
๐Review https://t.ly/L38iD
๐Project https://haar.is.tue.mpg.de/
๐Paper https://arxiv.org/pdf/2312.11666.pdf
๐Repo coming
๐คฏ4๐พ3๐2๐ฅ1
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๐ป GARField: Group Anything ๐ป
๐ GARField is a novel approach for decomposing #3D scenes into a hierarchy of semantically meaningful groups from posed image inputs.
๐Review https://t.ly/6Hkeq
๐Paper https://lnkd.in/d28mfRcZ
๐Project https://lnkd.in/dzYdRNKy
๐Repo (coming) https://lnkd.in/d2VeRJCS
๐ GARField is a novel approach for decomposing #3D scenes into a hierarchy of semantically meaningful groups from posed image inputs.
๐Review https://t.ly/6Hkeq
๐Paper https://lnkd.in/d28mfRcZ
๐Project https://lnkd.in/dzYdRNKy
๐Repo (coming) https://lnkd.in/d2VeRJCS
๐8โค3๐ฅฐ1๐คฉ1
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๐ฆGeometry Guided Depth๐ฆ
๐Depth and #3D reconstruction which can take as input, where available, previously-made estimates of the sceneโs geometry
๐Review https://lnkd.in/dMgakzWm
๐Paper https://arxiv.org/pdf/2406.18387
๐Repo (empty) https://github.com/nianticlabs/DoubleTake
๐Depth and #3D reconstruction which can take as input, where available, previously-made estimates of the sceneโs geometry
๐Review https://lnkd.in/dMgakzWm
๐Paper https://arxiv.org/pdf/2406.18387
๐Repo (empty) https://github.com/nianticlabs/DoubleTake
๐7๐ฅ7โค1๐ฅฐ1
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๐งคGigaHands: Massive #3D Hands๐งค
๐Novel massive #3D bimanual activities dataset: 34 hours of activities, 14k hand motions clips paired with 84k text annotation, 183M+ unique hand images
๐Review https://t.ly/SA0HG
๐Paper www.arxiv.org/pdf/2412.04244
๐Repo github.com/brown-ivl/gigahands
๐Project ivl.cs.brown.edu/research/gigahands.html
๐Novel massive #3D bimanual activities dataset: 34 hours of activities, 14k hand motions clips paired with 84k text annotation, 183M+ unique hand images
๐Review https://t.ly/SA0HG
๐Paper www.arxiv.org/pdf/2412.04244
๐Repo github.com/brown-ivl/gigahands
๐Project ivl.cs.brown.edu/research/gigahands.html
โค7๐1๐คฉ1
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โค๏ธโ๐ฅ Uncommon object in #3D โค๏ธโ๐ฅ
๐#META releases uCO3D, a new object-centric dataset for 3D AI. The largest publicly-available collection of HD videos of objects with 3D annotations that ensures full-360โฆ coverage. Code & data under CCA 4.0๐
๐Review https://t.ly/Z_tvA
๐Paper https://arxiv.org/pdf/2501.07574
๐Project https://uco3d.github.io/
๐Repo github.com/facebookresearch/uco3d
๐#META releases uCO3D, a new object-centric dataset for 3D AI. The largest publicly-available collection of HD videos of objects with 3D annotations that ensures full-360โฆ coverage. Code & data under CCA 4.0๐
๐Review https://t.ly/Z_tvA
๐Paper https://arxiv.org/pdf/2501.07574
๐Project https://uco3d.github.io/
๐Repo github.com/facebookresearch/uco3d
โค11โก2๐2๐1๐1๐คฉ1๐พ1
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๐LATTE-MV: #3D Table Tennis๐
๐UC Berkeley unveils at #CVPR2025 a novel system for reconstructing monocular video of table tennis in 3D with uncertainty-aware controller that anticipates opponent actions. Code & Dataset announced, to be released๐
๐Review https://t.ly/qPMOU
๐Paper arxiv.org/pdf/2503.20936
๐Project sastry-group.github.io/LATTE-MV/
๐Repo github.com/sastry-group/LATTE-MV
๐UC Berkeley unveils at #CVPR2025 a novel system for reconstructing monocular video of table tennis in 3D with uncertainty-aware controller that anticipates opponent actions. Code & Dataset announced, to be released๐
๐Review https://t.ly/qPMOU
๐Paper arxiv.org/pdf/2503.20936
๐Project sastry-group.github.io/LATTE-MV/
๐Repo github.com/sastry-group/LATTE-MV
๐ฅ8๐2๐1๐คฏ1
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๐งBoxDreamer Object Pose๐ง
๐BoxDreamer is a generalizable RGB-based approach for #3D object pose estimation in the wild, specifically designed to address challenges in sparse-view settings. Code coming, demo released๐
๐Review https://t.ly/e-vX9
๐Paper arxiv.org/pdf/2504.07955
๐Project https://lnkd.in/djz8jqn9
๐Repo https://lnkd.in/dfuEawSA
๐คDemo https://lnkd.in/dVYaWGcS
๐BoxDreamer is a generalizable RGB-based approach for #3D object pose estimation in the wild, specifically designed to address challenges in sparse-view settings. Code coming, demo released๐
๐Review https://t.ly/e-vX9
๐Paper arxiv.org/pdf/2504.07955
๐Project https://lnkd.in/djz8jqn9
๐Repo https://lnkd.in/dfuEawSA
๐คDemo https://lnkd.in/dVYaWGcS
โค3๐ฅ3๐2๐1
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๐ฅ Pose in Combat Sports ๐ฅ
๐The novel SOTA framework for an accurate physics-based #3D human pose estimation in combat sports w/ sparse multi-cameras setup. Dataset to be released soon๐
๐Review https://t.ly/EfcGL
๐Paper https://lnkd.in/deMMrKcA
๐Project https://lnkd.in/dkMS_UrH
๐The novel SOTA framework for an accurate physics-based #3D human pose estimation in combat sports w/ sparse multi-cameras setup. Dataset to be released soon๐
๐Review https://t.ly/EfcGL
๐Paper https://lnkd.in/deMMrKcA
๐Project https://lnkd.in/dkMS_UrH
๐13๐ฅ4โค3๐คฏ2
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๐PartField #3D Part Segmentation๐
๐#Nvidia unveils PartField, a FFW approach for learning part-based 3D features, which captures the general concept of parts and their hierarchy. Suitable for single-shape decomposition, co-segm., correspondence & more. Code & Models released under Nvidia License๐
๐Review https://t.ly/fGb2O
๐Paper https://lnkd.in/dGeyKSzG
๐Code https://lnkd.in/dbe57XGH
๐Project https://lnkd.in/dhEgf7X2
๐#Nvidia unveils PartField, a FFW approach for learning part-based 3D features, which captures the general concept of parts and their hierarchy. Suitable for single-shape decomposition, co-segm., correspondence & more. Code & Models released under Nvidia License๐
๐Review https://t.ly/fGb2O
๐Paper https://lnkd.in/dGeyKSzG
๐Code https://lnkd.in/dbe57XGH
๐Project https://lnkd.in/dhEgf7X2
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