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๐งคNeuro MusculoSkeletal-MANO๐งค
๐SJTU unveils MusculoSkeletal-MANO, novel musculoskeletal system with a learnable parametric hand model. Source Code announced ๐
๐Review https://t.ly/HOQrn
๐Paper arxiv.org/pdf/2404.10227.pdf
๐Project https://ms-mano.robotflow.ai/
๐Code announced (no repo yet)
๐SJTU unveils MusculoSkeletal-MANO, novel musculoskeletal system with a learnable parametric hand model. Source Code announced ๐
๐Review https://t.ly/HOQrn
๐Paper arxiv.org/pdf/2404.10227.pdf
๐Project https://ms-mano.robotflow.ai/
๐Code announced (no repo yet)
๐ฅ3โก1โค1๐1๐1
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โฝSoccerNET: Athlete Trackingโฝ
๐SoccerNet Challenge is a novel high level computer vision task that is specific to sports analytics. It aims at recognizing the state of a sport game, i.e., identifying and localizing all sports individuals (players, referees, ..) on the field.
๐Review https://t.ly/Mdu9s
๐Paper arxiv.org/pdf/2404.11335.pdf
๐Code github.com/SoccerNet/sn-gamestate
๐SoccerNet Challenge is a novel high level computer vision task that is specific to sports analytics. It aims at recognizing the state of a sport game, i.e., identifying and localizing all sports individuals (players, referees, ..) on the field.
๐Review https://t.ly/Mdu9s
๐Paper arxiv.org/pdf/2404.11335.pdf
๐Code github.com/SoccerNet/sn-gamestate
โค9๐8๐ฅ3โก2๐คฏ1
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๐ฒ Articulated Objs from MonoClips ๐ฒ
๐REACTO is the new SOTA to address the challenge of reconstructing general articulated 3D objects from single monocular video
๐Review https://t.ly/REuM8
๐Paper https://lnkd.in/d6PWagij
๐Project https://lnkd.in/dpg3x4tm
๐Repo https://lnkd.in/dRZWj6_N
๐REACTO is the new SOTA to address the challenge of reconstructing general articulated 3D objects from single monocular video
๐Review https://t.ly/REuM8
๐Paper https://lnkd.in/d6PWagij
๐Project https://lnkd.in/dpg3x4tm
๐Repo https://lnkd.in/dRZWj6_N
๐คฏ6๐1๐ฅ1๐1
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๐ชผ All You Need is SAM (+Flow) ๐ชผ
๐Oxford unveils the new SOTA for moving object segmentation via SAM + Optical Flow. Two novel models & Source Code announced ๐
๐Review https://t.ly/ZRYtp
๐Paper https://lnkd.in/d4XqkEGF
๐Project https://lnkd.in/dHpmx3FF
๐Repo coming: https://github.com/Jyxarthur/
๐Oxford unveils the new SOTA for moving object segmentation via SAM + Optical Flow. Two novel models & Source Code announced ๐
๐Review https://t.ly/ZRYtp
๐Paper https://lnkd.in/d4XqkEGF
๐Project https://lnkd.in/dHpmx3FF
๐Repo coming: https://github.com/Jyxarthur/
โค12๐7๐ฅ2๐คฏ2
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๐ 6Img-to-3D driving scenarios ๐
๐EPFL (+ Continental) unveils 6Img-to-3D, novel transformer-based encoder-renderer method to create 3D onbounded outdoor driving scenarios with only six pics
๐Review https://shorturl.at/dZ018
๐Paper arxiv.org/pdf/2404.12378.pdf
๐Project 6img-to-3d.github.io/
๐Code github.com/continental/6Img-to-3D
๐EPFL (+ Continental) unveils 6Img-to-3D, novel transformer-based encoder-renderer method to create 3D onbounded outdoor driving scenarios with only six pics
๐Review https://shorturl.at/dZ018
๐Paper arxiv.org/pdf/2404.12378.pdf
๐Project 6img-to-3d.github.io/
๐Code github.com/continental/6Img-to-3D
๐ฅ5โค1๐1
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๐น Physics-Based 3D Video-Gen ๐น
๐PhysDreamer, a physics-based approach that leverages the object dynamics priors learned by video generation models. It enables realistic 3D interaction with objects
๐Review https://t.ly/zxXf9
๐Paper arxiv.org/pdf/2404.13026.pdf
๐Project physdreamer.github.io/
๐Code github.com/a1600012888/PhysDreamer
๐PhysDreamer, a physics-based approach that leverages the object dynamics priors learned by video generation models. It enables realistic 3D interaction with objects
๐Review https://t.ly/zxXf9
๐Paper arxiv.org/pdf/2404.13026.pdf
๐Project physdreamer.github.io/
๐Code github.com/a1600012888/PhysDreamer
๐14โค9๐คฏ4๐1
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๐ก NER-Net: Seeing at Night-Time ๐ก
๐Huazhong (+Beijing) unveils a novel event-based nighttime imaging solution under non-uniform illumination, plus a paired multi-illumination level real-world dataset. Repo online, code coming ๐
๐Review https://t.ly/Z9JMJ
๐Paper arxiv.org/pdf/2404.11884.pdf
๐Repo github.com/Liu-haoyue/NER-Net
๐Clip https://www.youtube.com/watch?v=zpfTLCF1Kw4
๐Huazhong (+Beijing) unveils a novel event-based nighttime imaging solution under non-uniform illumination, plus a paired multi-illumination level real-world dataset. Repo online, code coming ๐
๐Review https://t.ly/Z9JMJ
๐Paper arxiv.org/pdf/2404.11884.pdf
๐Repo github.com/Liu-haoyue/NER-Net
๐Clip https://www.youtube.com/watch?v=zpfTLCF1Kw4
๐คฏ3๐ฅ2โค1๐1
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๐ FlowMap: dense depth video ๐
๐MIT (+CSAIL) unveils FlowMap, a novel E2E differentiable method that solves for precise camera poses, camera intrinsics, and perframe dense depth of a video sequence. Source Code released ๐
๐Review https://t.ly/CBH48
๐Paper arxiv.org/pdf/2404.15259.pdf
๐Project cameronosmith.github.io/flowmap
๐Code github.com/dcharatan/flowmap
๐MIT (+CSAIL) unveils FlowMap, a novel E2E differentiable method that solves for precise camera poses, camera intrinsics, and perframe dense depth of a video sequence. Source Code released ๐
๐Review https://t.ly/CBH48
๐Paper arxiv.org/pdf/2404.15259.pdf
๐Project cameronosmith.github.io/flowmap
๐Code github.com/dcharatan/flowmap
๐ฅ18โค3๐2
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๐TELA: Text to 3D Clothed Human๐
๐ TELA is a novel approach for the new task of clothing disentangled 3D human model generation from texts. This novel approach unleashes the potential of many downstream applications (e.g., virtual try-on).
๐Review https://t.ly/6N7JV
๐Paper https://arxiv.org/pdf/2404.16748
๐Project https://jtdong.com/tela_layer/
๐Code https://github.com/DongJT1996/TELA
๐ TELA is a novel approach for the new task of clothing disentangled 3D human model generation from texts. This novel approach unleashes the potential of many downstream applications (e.g., virtual try-on).
๐Review https://t.ly/6N7JV
๐Paper https://arxiv.org/pdf/2404.16748
๐Project https://jtdong.com/tela_layer/
๐Code https://github.com/DongJT1996/TELA
๐5๐ฅ4๐คฏ3๐1๐พ1
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๐ชท Tunnel Try-on: SOTA VTON ๐ชท
๐"Tunnel Try-on", the first diffusion-based video virtual try-on model that demonstrates SOTA performance in complex scenarios. No code announced :(
๐Review https://t.ly/joMtJ
๐Paper arxiv.org/pdf/2404.17571
๐Project mengtingchen.github.io/tunnel-try-on-page/
๐"Tunnel Try-on", the first diffusion-based video virtual try-on model that demonstrates SOTA performance in complex scenarios. No code announced :(
๐Review https://t.ly/joMtJ
๐Paper arxiv.org/pdf/2404.17571
๐Project mengtingchen.github.io/tunnel-try-on-page/
โค9๐ฅ4๐1๐ฅฐ1๐พ1
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๐๏ธ1000x Scalable Neural 3D Fields๐๏ธ
๐Highly-scalable neural 3D Fields: 1000x reductions in memory maintaining speed/quality: 10 MB vs. 10 GB! Code released ๐
๐Review https://t.ly/sLTK5
๐Paper https://lnkd.in/dEYM8-t2
๐Project https://lnkd.in/djptdujx
๐Code https://lnkd.in/dcCnFZ2n
๐Highly-scalable neural 3D Fields: 1000x reductions in memory maintaining speed/quality: 10 MB vs. 10 GB! Code released ๐
๐Review https://t.ly/sLTK5
๐Paper https://lnkd.in/dEYM8-t2
๐Project https://lnkd.in/djptdujx
๐Code https://lnkd.in/dcCnFZ2n
๐คฏ13๐5๐ฅ4โค3๐ฅฐ1
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๐3D Scenes w/ Depth Inpainting๐
๐Oxford announced two novel contributions to the field of 3D scene generation: a new benchmark and a novel depth completion model. ๐ค-Demo and Source Code released๐
๐Review https://t.ly/BKiny
๐Paper arxiv.org/pdf/2404.19758
๐Project research.paulengstler.com/invisible-stitch/
๐Code github.com/paulengstler/invisible-stitch
๐Demo huggingface.co/spaces/paulengstler/invisible-stitch
๐Oxford announced two novel contributions to the field of 3D scene generation: a new benchmark and a novel depth completion model. ๐ค-Demo and Source Code released๐
๐Review https://t.ly/BKiny
๐Paper arxiv.org/pdf/2404.19758
๐Project research.paulengstler.com/invisible-stitch/
๐Code github.com/paulengstler/invisible-stitch
๐Demo huggingface.co/spaces/paulengstler/invisible-stitch
โค3๐2๐1๐ฅ1๐ฅฐ1๐คฏ1๐พ1
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๐ Diffusive 3D Human Recovery ๐
๐The Rutgers University unveils ScoreHMR at #CVPR24; novel approach for 3D human pose and shape reconstruction. Impressive results.
๐Review https://t.ly/G0k2D
๐Paper https://arxiv.org/pdf/2403.09623
๐Code https://github.com/statho/ScoreHMR
๐Project https://statho.github.io/ScoreHMR/
๐The Rutgers University unveils ScoreHMR at #CVPR24; novel approach for 3D human pose and shape reconstruction. Impressive results.
๐Review https://t.ly/G0k2D
๐Paper https://arxiv.org/pdf/2403.09623
๐Code https://github.com/statho/ScoreHMR
๐Project https://statho.github.io/ScoreHMR/
๐คฏ11๐6โค1๐1๐คฃ1
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๐ท๏ธDiffMOT (#CVPR24): diffusion-MOT๐ท๏ธ
๐DiffMOT is a novel real-time diffusion-based MOT approach to tackle the complex nonlinear motion. Impressive results & Source Code released๐
๐Review https://t.ly/ztlHi
๐Paper https://lnkd.in/d4K3c-nt
๐Project https://diffmot.github.io/
๐Code github.com/Kroery/DiffMOT
๐DiffMOT is a novel real-time diffusion-based MOT approach to tackle the complex nonlinear motion. Impressive results & Source Code released๐
๐Review https://t.ly/ztlHi
๐Paper https://lnkd.in/d4K3c-nt
๐Project https://diffmot.github.io/
๐Code github.com/Kroery/DiffMOT
โค12๐4๐ฅ3๐คฏ3
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๐ XFeat: Neural Features Matching ๐
๐XFeat (Accelerated Features) is lightweight/accurate architecture for efficient visual correspondence. It revisits fundamental design choices in CNN for detecting, extracting & matching local features
๐Review https://t.ly/ppb38
๐Paper arxiv.org/pdf/2404.19174
๐Code https://lnkd.in/dFzTpzN8
๐Project https://lnkd.in/d8JnV-iu
๐XFeat (Accelerated Features) is lightweight/accurate architecture for efficient visual correspondence. It revisits fundamental design choices in CNN for detecting, extracting & matching local features
๐Review https://t.ly/ppb38
๐Paper arxiv.org/pdf/2404.19174
๐Code https://lnkd.in/dFzTpzN8
๐Project https://lnkd.in/d8JnV-iu
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๐ฆ Hyper-Detailed Image Descriptions ๐ฆ
๐#Google unveils ImageInWords (IIW), a carefully designed HIL annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process
๐Review https://t.ly/engkl
๐Paper arxiv.org/pdf/2405.02793
๐Repo github.com/google/imageinwords
๐Project google.github.io/imageinwords
๐Data huggingface.co/datasets/google/imageinwords
๐#Google unveils ImageInWords (IIW), a carefully designed HIL annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process
๐Review https://t.ly/engkl
๐Paper arxiv.org/pdf/2405.02793
๐Repo github.com/google/imageinwords
๐Project google.github.io/imageinwords
๐Data huggingface.co/datasets/google/imageinwords
โค11๐ฅ3๐2๐คฏ2๐พ1
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๐ซ Free-Moving Reconstruction ๐ซ
๐EPFL (+#MagicLeap) unveils a novel approach for reconstructing free-moving object from monocular RGB clip. Free interaction with objects in front of a moving cam without relying on any prior, and optimizes the sequence globally without any segments. Great but no code announced๐ฅบ
๐Review https://t.ly/2xhtj
๐Paper arxiv.org/pdf/2405.05858
๐Project haixinshi.github.io/fmov/
๐EPFL (+#MagicLeap) unveils a novel approach for reconstructing free-moving object from monocular RGB clip. Free interaction with objects in front of a moving cam without relying on any prior, and optimizes the sequence globally without any segments. Great but no code announced๐ฅบ
๐Review https://t.ly/2xhtj
๐Paper arxiv.org/pdf/2405.05858
๐Project haixinshi.github.io/fmov/
๐6๐คฏ4โก1โค1๐ฅฐ1
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๐ฅFeatUp: Any Model at Any Resolution๐ฅ
๐FeatUp is a task-model agnostic framework to restore lost spatial information in deep features. It outperforms other methods in class activation map generation, transfer learning for segmentation & depth, and end-to-end training for semantic segm. Source Code released๐
๐Review https://t.ly/Evq_g
๐Paper https://lnkd.in/gweaN4s6
๐Project https://lnkd.in/gWcGXdxt
๐Code https://lnkd.in/gweq5NY4
๐FeatUp is a task-model agnostic framework to restore lost spatial information in deep features. It outperforms other methods in class activation map generation, transfer learning for segmentation & depth, and end-to-end training for semantic segm. Source Code released๐
๐Review https://t.ly/Evq_g
๐Paper https://lnkd.in/gweaN4s6
๐Project https://lnkd.in/gWcGXdxt
๐Code https://lnkd.in/gweq5NY4
๐ฅ19โค4๐3๐1๐พ1
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๐AniTalker: Universal Talking Humans๐
๐SJTU (+AISpeech) unveils AniTalker, a framework that transforms a single static portrait and input audio into animated talking videos with naturally flowing movements.
๐Review https://t.ly/MD4yX
๐Paper https://arxiv.org/pdf/2405.03121
๐Project https://x-lance.github.io/AniTalker/
๐Repo https://github.com/X-LANCE/AniTalker
๐SJTU (+AISpeech) unveils AniTalker, a framework that transforms a single static portrait and input audio into animated talking videos with naturally flowing movements.
๐Review https://t.ly/MD4yX
๐Paper https://arxiv.org/pdf/2405.03121
๐Project https://x-lance.github.io/AniTalker/
๐Repo https://github.com/X-LANCE/AniTalker
๐ฅ6โค4๐2โก1๐คฏ1
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๐ป 3D Humans Motion from Text ๐ป
๐Zhejiang (+ANT) unveils a novel method to generate human motions containing accurate human-object interactions in 3D scenes based on textural descriptions. Code announced, coming ๐
๐Review https://t.ly/eOZnU
๐Paper https://arxiv.org/pdf/2405.07784
๐Project https://zju3dv.github.io/text_scene_motion/
๐Zhejiang (+ANT) unveils a novel method to generate human motions containing accurate human-object interactions in 3D scenes based on textural descriptions. Code announced, coming ๐
๐Review https://t.ly/eOZnU
๐Paper https://arxiv.org/pdf/2405.07784
๐Project https://zju3dv.github.io/text_scene_motion/
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