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đǍ Google URF for neural-synthesis đǍ
đSequence of RGB + Lidar -> 3D surfaces and novel RGB images synthesized
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Extending Neural Radiance Fields
â Leveraging asynch. lidar data
â Addressing exposure variation
â Leveraging segmentations for sky
â SOTA #3D reconstructions/synthesizes
More: https://bit.ly/3L2vTDb
đSequence of RGB + Lidar -> 3D surfaces and novel RGB images synthesized
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Extending Neural Radiance Fields
â Leveraging asynch. lidar data
â Addressing exposure variation
â Leveraging segmentations for sky
â SOTA #3D reconstructions/synthesizes
More: https://bit.ly/3L2vTDb
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đ AV2: next-gen. self driving đ
đOne of the biggest dataset ever for #autonomousdriving
đđĸđ đĄđĨđĸđ đĄđđŦ:
â 1k seq. of multimodal data
â 3D annotations, 26 categories
â 20k lidar & map-aligned pose
â 250k challenging interactions
â HD Map: 3D lane & crosswalk
â CC BY-NC-SA 4.0 license
More: https://bit.ly/3trx3lw
đOne of the biggest dataset ever for #autonomousdriving
đđĸđ đĄđĨđĸđ đĄđđŦ:
â 1k seq. of multimodal data
â 3D annotations, 26 categories
â 20k lidar & map-aligned pose
â 250k challenging interactions
â HD Map: 3D lane & crosswalk
â CC BY-NC-SA 4.0 license
More: https://bit.ly/3trx3lw
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đ¤CaTGrasp in Clutter from Simulationđ¤
đTask-relevant grasping: trained solely in simulation with synthetic + SS. hand-object interaction
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Novel cat-level, relevant grasping
â S.S. hand-object-contact
â Tiny objects from dense clutter
â Train-simulation -> to real
â Source code under Apache 2.0
More: https://bit.ly/3L2YVCo
đTask-relevant grasping: trained solely in simulation with synthetic + SS. hand-object interaction
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Novel cat-level, relevant grasping
â S.S. hand-object-contact
â Tiny objects from dense clutter
â Train-simulation -> to real
â Source code under Apache 2.0
More: https://bit.ly/3L2YVCo
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đŧ Drive & Segment without Supervision đŧ
đLearning pixel-wise semantic seg. on non-curated data collection by cars (cameras + LiDAR) driving around a city
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Cross-modal unsupervised
â Synchronized LiDAR & RGB
â Object proposal on LiDAR points
â SOTA, significant improvements
More: https://bit.ly/3L0wWTW
đLearning pixel-wise semantic seg. on non-curated data collection by cars (cameras + LiDAR) driving around a city
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Cross-modal unsupervised
â Synchronized LiDAR & RGB
â Object proposal on LiDAR points
â SOTA, significant improvements
More: https://bit.ly/3L0wWTW
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đ NeRF-free Neural Rendering đ
đA simple 2D-only method with a single pass of a neural network
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Synthesis with NO 3D reasoning
â Autoregressive & masked transf.
â Pose -> object, object -> pose
â Attention: branching attention
â Source code under MIT License
More: https://bit.ly/3JC7unt
đA simple 2D-only method with a single pass of a neural network
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Synthesis with NO 3D reasoning
â Autoregressive & masked transf.
â Pose -> object, object -> pose
â Attention: branching attention
â Source code under MIT License
More: https://bit.ly/3JC7unt
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đ¤đHey, TAKE OFF my eyeglasses! đđ
đA novel framework to remove eyeglasses as well as their cast shadows from faces
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Novel mask-guided multi-step network
â Leveraging 3D synthetic data only
â Synthetic portraits with supervisions
â Eyeglasses & shadows simultaneously
More: https://bit.ly/3IvQzlf
đA novel framework to remove eyeglasses as well as their cast shadows from faces
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Novel mask-guided multi-step network
â Leveraging 3D synthetic data only
â Synthetic portraits with supervisions
â Eyeglasses & shadows simultaneously
More: https://bit.ly/3IvQzlf
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đĨ #AI models/dataset for open surgery đĨ
đMulti-task #AI model/dataset of real-time surgical behaviors, hands, and tools.
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Annotated Videos Open Surgery
â Largest dataset of open surgical
â 2k clips and 23 procedures
â 12k annotations, 11k+ keypoints
â Models/Dataset soon available!
More: https://bit.ly/3tvDdkK
đMulti-task #AI model/dataset of real-time surgical behaviors, hands, and tools.
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Annotated Videos Open Surgery
â Largest dataset of open surgical
â 2k clips and 23 procedures
â 12k annotations, 11k+ keypoints
â Models/Dataset soon available!
More: https://bit.ly/3tvDdkK
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đĨŊ #metaverse in 1991 đĨŊ
đQ: is #VR the technology that developed least in the last 30 years? đ¤
Discussion: https://bit.ly/3txWF07
đQ: is #VR the technology that developed least in the last 30 years? đ¤
Discussion: https://bit.ly/3txWF07
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đĢNeRFusion: Large-Scale ReconstructionđĢ
đEfficient large-scale reconstruction & photo-realistic rendering
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Frame-by-frame R.F.
â Neural reconstruction
â Real-time at 20+ fps
â SOTA on indoor / objects
More: https://bit.ly/3iyfoCo
đEfficient large-scale reconstruction & photo-realistic rendering
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Frame-by-frame R.F.
â Neural reconstruction
â Real-time at 20+ fps
â SOTA on indoor / objects
More: https://bit.ly/3iyfoCo
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âORViT for understanding tasksâ
đORViT: object-centric approach that extends ViT layers incorporating object representations
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Spatio-temporal through the net
â ''Object-Region Attention''
â ''Object-Dynamics" module
â Code just released! Apache 2.0
More: https://bit.ly/3wAUavW
đORViT: object-centric approach that extends ViT layers incorporating object representations
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Spatio-temporal through the net
â ''Object-Region Attention''
â ''Object-Dynamics" module
â Code just released! Apache 2.0
More: https://bit.ly/3wAUavW
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đĒ
Insane Neural Sketching from #MITđĒ
đLine drawing generation as unsupervised image translation with various losses
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Unpaired method for line drawing
â Geometry loss to predict depth
â Semantic loss to match CLIP feats
â SOTA on unpaired translation/generation
â Code and Models under MIT License
More: https://bit.ly/36JRr8A
đLine drawing generation as unsupervised image translation with various losses
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Unpaired method for line drawing
â Geometry loss to predict depth
â Semantic loss to match CLIP feats
â SOTA on unpaired translation/generation
â Code and Models under MIT License
More: https://bit.ly/36JRr8A
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đī¸MPS-Net: new SOTA for #3D humanđī¸
đMPS-Net: accurate & temporally coherent 3D human pose/shape from video
đđĸđ đĄđĨđĸđ đĄđđŦ:
â MoCA: visual cues from motion
â HAFI to mix past/future feats
â Stronger temporal correlation
â SOTA on multiple datasets
More: https://bit.ly/3uAI5EB
đMPS-Net: accurate & temporally coherent 3D human pose/shape from video
đđĸđ đĄđĨđĸđ đĄđđŦ:
â MoCA: visual cues from motion
â HAFI to mix past/future feats
â Stronger temporal correlation
â SOTA on multiple datasets
More: https://bit.ly/3uAI5EB
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đ¤ŋTransfiner: hyper-detailed segmentationđ¤ŋ
đMask Transfiner: #AI for HQ & efficient instance segmentation
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Transfiner: HQ segmentation
â HQ seg. via quadtree structure
â SOTA & extreme details
â Code under MIT License
More: https://bit.ly/3KVzseM
đMask Transfiner: #AI for HQ & efficient instance segmentation
đđĸđ đĄđĨđĸđ đĄđđŦ:
â Transfiner: HQ segmentation
â HQ seg. via quadtree structure
â SOTA & extreme details
â Code under MIT License
More: https://bit.ly/3KVzseM
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đĨ DualStyleGAN: SOTA in style transferđĨ
đFlexible control of dual styles of face domain and extended artistic portrait domain
đđĸđ đĄđĨđĸđ đĄđđŦ:
â High-resolution (1024*1024)
â Intrinsic/extrinsic style path
â Hierarchical style manipulation
â Novel progressive fine-tuning
â Source code under MIT License
More: https://bit.ly/3uS26Xp
đFlexible control of dual styles of face domain and extended artistic portrait domain
đđĸđ đĄđĨđĸđ đĄđđŦ:
â High-resolution (1024*1024)
â Intrinsic/extrinsic style path
â Hierarchical style manipulation
â Novel progressive fine-tuning
â Source code under MIT License
More: https://bit.ly/3uS26Xp
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đ GTR: Global Tracking Transformers đ
đUTexas + Apple: transformer for global multi-object tracking
đđĸđ đĄđĨđĸđ đĄđđŦ:
â GTR operates on any object
â Few frames->global trajectories
â SOTA on detectors for any object
â Code under Apache License 2.0
More: https://bit.ly/3DiqkxF
đUTexas + Apple: transformer for global multi-object tracking
đđĸđ đĄđĨđĸđ đĄđđŦ:
â GTR operates on any object
â Few frames->global trajectories
â SOTA on detectors for any object
â Code under Apache License 2.0
More: https://bit.ly/3DiqkxF
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đ§ E2E Perception for #selfdrivingcarsđ§
đHybridNets: multi-task net with several key optimizations
đđĸđ đĄđĨđĸđ đĄđđŦ:
â End-to-end perception network
â Traffic, lane, object detection
â Drivable segmentation area
â Real-time on embedded systems
â Source code under MIT License
More: https://bit.ly/3JMk8Az
đHybridNets: multi-task net with several key optimizations
đđĸđ đĄđĨđĸđ đĄđđŦ:
â End-to-end perception network
â Traffic, lane, object detection
â Drivable segmentation area
â Real-time on embedded systems
â Source code under MIT License
More: https://bit.ly/3JMk8Az
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đŠī¸Smart Parking with UAVsđŠī¸
đA novel methodology to monitor car parking areas in real-time via Drones/UAVs
đđĸđ đĄđĨđĸđ đĄđđŦ:
â YoloV3 + DeepSort tracker
â Vehicle detection/tracking
â Occupancy estimation via RT
â Four blocks, unique pipeline
More: https://bit.ly/3iJD8nm
đA novel methodology to monitor car parking areas in real-time via Drones/UAVs
đđĸđ đĄđĨđĸđ đĄđđŦ:
â YoloV3 + DeepSort tracker
â Vehicle detection/tracking
â Occupancy estimation via RT
â Four blocks, unique pipeline
More: https://bit.ly/3iJD8nm
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đ Detecting Events via #AI đ
đLocalizing object states & corresponding state-modifying actions
đđĸđ đĄđĨđĸđ đĄđđŦ:
â SS-learning state-modifying
â Noise adaptive weighting
â ChangeIt: 2.6k+ hrs , 34k+ changes
â Dataset, code, and model!
More: https://bit.ly/3uBwxkj
đLocalizing object states & corresponding state-modifying actions
đđĸđ đĄđĨđĸđ đĄđđŦ:
â SS-learning state-modifying
â Noise adaptive weighting
â ChangeIt: 2.6k+ hrs , 34k+ changes
â Dataset, code, and model!
More: https://bit.ly/3uBwxkj
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đđ Interactive Neural Labelling đđ
đDense labelling of geometry, color & semantics via #3D neural field
đđĸđ đĄđĨđĸđ đĄđđŦ:
â No training data
â Dense labeling
â Classes on the fly
â Labelling at a scale
More: https://bit.ly/36Y0faQ
đDense labelling of geometry, color & semantics via #3D neural field
đđĸđ đĄđĨđĸđ đĄđđŦ:
â No training data
â Dense labeling
â Classes on the fly
â Labelling at a scale
More: https://bit.ly/36Y0faQ
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âī¸Neural RGB-D Reconstructionâī¸
đNovel approach for #3D mixing implicit surface representations with NeRFs
đđĸđ đĄđĨđĸđ đĄđđŦ:
â RGB-D based reconstruction
â Leveraging color & depth
â Depth into the NeRF
â Pose & camera refinement
More: https://bit.ly/3iN6e54
đNovel approach for #3D mixing implicit surface representations with NeRFs
đđĸđ đĄđĨđĸđ đĄđđŦ:
â RGB-D based reconstruction
â Leveraging color & depth
â Depth into the NeRF
â Pose & camera refinement
More: https://bit.ly/3iN6e54
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