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🐯 S.S. Latent Image Animator 🐯
👉Self-supervised autoencoder to animate unseen images by linear navigation in latent
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Latent Image Animator
✅Linear displacement in latent
✅SOTA: VoxCeleb, Taichi, TED-talk
✅Source code (soon) available
More: https://bit.ly/36pgLAC
👉Self-supervised autoencoder to animate unseen images by linear navigation in latent
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Latent Image Animator
✅Linear displacement in latent
✅SOTA: VoxCeleb, Taichi, TED-talk
✅Source code (soon) available
More: https://bit.ly/36pgLAC
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🪨 Google URF for neural-synthesis 🪨
👉Sequence of RGB + Lidar -> 3D surfaces and novel RGB images synthesized
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Extending Neural Radiance Fields
✅Leveraging asynch. lidar data
✅Addressing exposure variation
✅Leveraging segmentations for sky
✅SOTA #3D reconstructions/synthesizes
More: https://bit.ly/3L2vTDb
👉Sequence of RGB + Lidar -> 3D surfaces and novel RGB images synthesized
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Extending Neural Radiance Fields
✅Leveraging asynch. lidar data
✅Addressing exposure variation
✅Leveraging segmentations for sky
✅SOTA #3D reconstructions/synthesizes
More: https://bit.ly/3L2vTDb
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🚛 AV2: next-gen. self driving 🚛
👉One of the biggest dataset ever for #autonomousdriving
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅1k seq. of multimodal data
✅3D annotations, 26 categories
✅20k lidar & map-aligned pose
✅250k challenging interactions
✅HD Map: 3D lane & crosswalk
✅CC BY-NC-SA 4.0 license
More: https://bit.ly/3trx3lw
👉One of the biggest dataset ever for #autonomousdriving
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅1k seq. of multimodal data
✅3D annotations, 26 categories
✅20k lidar & map-aligned pose
✅250k challenging interactions
✅HD Map: 3D lane & crosswalk
✅CC BY-NC-SA 4.0 license
More: https://bit.ly/3trx3lw
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🤖CaTGrasp in Clutter from Simulation🤖
👉Task-relevant grasping: trained solely in simulation with synthetic + SS. hand-object interaction
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel cat-level, relevant grasping
✅S.S. hand-object-contact
✅Tiny objects from dense clutter
✅Train-simulation -> to real
✅Source code under Apache 2.0
More: https://bit.ly/3L2YVCo
👉Task-relevant grasping: trained solely in simulation with synthetic + SS. hand-object interaction
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel cat-level, relevant grasping
✅S.S. hand-object-contact
✅Tiny objects from dense clutter
✅Train-simulation -> to real
✅Source code under Apache 2.0
More: https://bit.ly/3L2YVCo
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🛼 Drive & Segment without Supervision 🛼
👉Learning pixel-wise semantic seg. on non-curated data collection by cars (cameras + LiDAR) driving around a city
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Cross-modal unsupervised
✅Synchronized LiDAR & RGB
✅Object proposal on LiDAR points
✅SOTA, significant improvements
More: https://bit.ly/3L0wWTW
👉Learning pixel-wise semantic seg. on non-curated data collection by cars (cameras + LiDAR) driving around a city
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Cross-modal unsupervised
✅Synchronized LiDAR & RGB
✅Object proposal on LiDAR points
✅SOTA, significant improvements
More: https://bit.ly/3L0wWTW
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🌍 NeRF-free Neural Rendering 🌍
👉A simple 2D-only method with a single pass of a neural network
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Synthesis with NO 3D reasoning
✅Autoregressive & masked transf.
✅Pose -> object, object -> pose
✅Attention: branching attention
✅Source code under MIT License
More: https://bit.ly/3JC7unt
👉A simple 2D-only method with a single pass of a neural network
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Synthesis with NO 3D reasoning
✅Autoregressive & masked transf.
✅Pose -> object, object -> pose
✅Attention: branching attention
✅Source code under MIT License
More: https://bit.ly/3JC7unt
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🤓👌Hey, TAKE OFF my eyeglasses! 😙👌
👉A novel framework to remove eyeglasses as well as their cast shadows from faces
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel mask-guided multi-step network
✅Leveraging 3D synthetic data only
✅Synthetic portraits with supervisions
✅Eyeglasses & shadows simultaneously
More: https://bit.ly/3IvQzlf
👉A novel framework to remove eyeglasses as well as their cast shadows from faces
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel mask-guided multi-step network
✅Leveraging 3D synthetic data only
✅Synthetic portraits with supervisions
✅Eyeglasses & shadows simultaneously
More: https://bit.ly/3IvQzlf
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🏥 #AI models/dataset for open surgery 🏥
👉Multi-task #AI model/dataset of real-time surgical behaviors, hands, and tools.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Annotated Videos Open Surgery
✅Largest dataset of open surgical
✅2k clips and 23 procedures
✅12k annotations, 11k+ keypoints
✅Models/Dataset soon available!
More: https://bit.ly/3tvDdkK
👉Multi-task #AI model/dataset of real-time surgical behaviors, hands, and tools.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Annotated Videos Open Surgery
✅Largest dataset of open surgical
✅2k clips and 23 procedures
✅12k annotations, 11k+ keypoints
✅Models/Dataset soon available!
More: https://bit.ly/3tvDdkK
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🥽 #metaverse in 1991 🥽
👉Q: is #VR the technology that developed least in the last 30 years? 🤔
Discussion: https://bit.ly/3txWF07
👉Q: is #VR the technology that developed least in the last 30 years? 🤔
Discussion: https://bit.ly/3txWF07
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🫕NeRFusion: Large-Scale Reconstruction🫕
👉Efficient large-scale reconstruction & photo-realistic rendering
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Frame-by-frame R.F.
✅Neural reconstruction
✅Real-time at 20+ fps
✅SOTA on indoor / objects
More: https://bit.ly/3iyfoCo
👉Efficient large-scale reconstruction & photo-realistic rendering
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Frame-by-frame R.F.
✅Neural reconstruction
✅Real-time at 20+ fps
✅SOTA on indoor / objects
More: https://bit.ly/3iyfoCo
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☕ORViT for understanding tasks☕
👉ORViT: object-centric approach that extends ViT layers incorporating object representations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Spatio-temporal through the net
✅''Object-Region Attention''
✅''Object-Dynamics" module
✅Code just released! Apache 2.0
More: https://bit.ly/3wAUavW
👉ORViT: object-centric approach that extends ViT layers incorporating object representations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Spatio-temporal through the net
✅''Object-Region Attention''
✅''Object-Dynamics" module
✅Code just released! Apache 2.0
More: https://bit.ly/3wAUavW
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🪅Insane Neural Sketching from #MIT🪅
👉Line drawing generation as unsupervised image translation with various losses
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unpaired method for line drawing
✅Geometry loss to predict depth
✅Semantic loss to match CLIP feats
✅SOTA on unpaired translation/generation
✅Code and Models under MIT License
More: https://bit.ly/36JRr8A
👉Line drawing generation as unsupervised image translation with various losses
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unpaired method for line drawing
✅Geometry loss to predict depth
✅Semantic loss to match CLIP feats
✅SOTA on unpaired translation/generation
✅Code and Models under MIT License
More: https://bit.ly/36JRr8A
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🏔️MPS-Net: new SOTA for #3D human🏔️
👉MPS-Net: accurate & temporally coherent 3D human pose/shape from video
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅MoCA: visual cues from motion
✅HAFI to mix past/future feats
✅Stronger temporal correlation
✅SOTA on multiple datasets
More: https://bit.ly/3uAI5EB
👉MPS-Net: accurate & temporally coherent 3D human pose/shape from video
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅MoCA: visual cues from motion
✅HAFI to mix past/future feats
✅Stronger temporal correlation
✅SOTA on multiple datasets
More: https://bit.ly/3uAI5EB
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🤿Transfiner: hyper-detailed segmentation🤿
👉Mask Transfiner: #AI for HQ & efficient instance segmentation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Transfiner: HQ segmentation
✅HQ seg. via quadtree structure
✅SOTA & extreme details
✅Code under MIT License
More: https://bit.ly/3KVzseM
👉Mask Transfiner: #AI for HQ & efficient instance segmentation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Transfiner: HQ segmentation
✅HQ seg. via quadtree structure
✅SOTA & extreme details
✅Code under MIT License
More: https://bit.ly/3KVzseM
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🥙 DualStyleGAN: SOTA in style transfer🥙
👉Flexible control of dual styles of face domain and extended artistic portrait domain
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅High-resolution (1024*1024)
✅Intrinsic/extrinsic style path
✅Hierarchical style manipulation
✅Novel progressive fine-tuning
✅Source code under MIT License
More: https://bit.ly/3uS26Xp
👉Flexible control of dual styles of face domain and extended artistic portrait domain
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅High-resolution (1024*1024)
✅Intrinsic/extrinsic style path
✅Hierarchical style manipulation
✅Novel progressive fine-tuning
✅Source code under MIT License
More: https://bit.ly/3uS26Xp
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🍚 GTR: Global Tracking Transformers 🍚
👉UTexas + Apple: transformer for global multi-object tracking
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅GTR operates on any object
✅Few frames->global trajectories
✅SOTA on detectors for any object
✅Code under Apache License 2.0
More: https://bit.ly/3DiqkxF
👉UTexas + Apple: transformer for global multi-object tracking
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅GTR operates on any object
✅Few frames->global trajectories
✅SOTA on detectors for any object
✅Code under Apache License 2.0
More: https://bit.ly/3DiqkxF
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🧠E2E Perception for #selfdrivingcars🧠
👉HybridNets: multi-task net with several key optimizations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅End-to-end perception network
✅Traffic, lane, object detection
✅Drivable segmentation area
✅Real-time on embedded systems
✅Source code under MIT License
More: https://bit.ly/3JMk8Az
👉HybridNets: multi-task net with several key optimizations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅End-to-end perception network
✅Traffic, lane, object detection
✅Drivable segmentation area
✅Real-time on embedded systems
✅Source code under MIT License
More: https://bit.ly/3JMk8Az
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🛩️Smart Parking with UAVs🛩️
👉A novel methodology to monitor car parking areas in real-time via Drones/UAVs
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅YoloV3 + DeepSort tracker
✅Vehicle detection/tracking
✅Occupancy estimation via RT
✅Four blocks, unique pipeline
More: https://bit.ly/3iJD8nm
👉A novel methodology to monitor car parking areas in real-time via Drones/UAVs
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅YoloV3 + DeepSort tracker
✅Vehicle detection/tracking
✅Occupancy estimation via RT
✅Four blocks, unique pipeline
More: https://bit.ly/3iJD8nm
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👕 Detecting Events via #AI 👕
👉Localizing object states & corresponding state-modifying actions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅SS-learning state-modifying
✅Noise adaptive weighting
✅ChangeIt: 2.6k+ hrs , 34k+ changes
✅Dataset, code, and model!
More: https://bit.ly/3uBwxkj
👉Localizing object states & corresponding state-modifying actions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅SS-learning state-modifying
✅Noise adaptive weighting
✅ChangeIt: 2.6k+ hrs , 34k+ changes
✅Dataset, code, and model!
More: https://bit.ly/3uBwxkj
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🌈🌈 Interactive Neural Labelling 🌈🌈
👉Dense labelling of geometry, color & semantics via #3D neural field
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅No training data
✅Dense labeling
✅Classes on the fly
✅Labelling at a scale
More: https://bit.ly/36Y0faQ
👉Dense labelling of geometry, color & semantics via #3D neural field
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅No training data
✅Dense labeling
✅Classes on the fly
✅Labelling at a scale
More: https://bit.ly/36Y0faQ
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♟️Neural RGB-D Reconstruction♟️
👉Novel approach for #3D mixing implicit surface representations with NeRFs
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅RGB-D based reconstruction
✅Leveraging color & depth
✅Depth into the NeRF
✅Pose & camera refinement
More: https://bit.ly/3iN6e54
👉Novel approach for #3D mixing implicit surface representations with NeRFs
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅RGB-D based reconstruction
✅Leveraging color & depth
✅Depth into the NeRF
✅Pose & camera refinement
More: https://bit.ly/3iN6e54
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