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πAnimated hand in 1972, damn romanticπ
πQ: is #VR the technology that developed least in the last 30 years? π€
More: https://bit.ly/3snxNaq
πQ: is #VR the technology that developed least in the last 30 years? π€
More: https://bit.ly/3snxNaq
π7β€3π€―1
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βοΈEnsembling models for GAN trainingβοΈ
πPretrained vision models to improve the GAN training. FID by 1.5 to 2Γ!
ππ’π π‘π₯π’π π‘ππ¬:
β CV models as ensemble of discriminators
β Improving GAN in limited / large-scale set
β 10k samples matches StyleGAN2 w/ 1.6M
β Source code / models under MIT license
More: https://bit.ly/3wgUVsr
πPretrained vision models to improve the GAN training. FID by 1.5 to 2Γ!
ππ’π π‘π₯π’π π‘ππ¬:
β CV models as ensemble of discriminators
β Improving GAN in limited / large-scale set
β 10k samples matches StyleGAN2 w/ 1.6M
β Source code / models under MIT license
More: https://bit.ly/3wgUVsr
π€―6π₯2
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π€―Cooperative Driving + AUTOCASTSIMπ€―
πCOOPERNAUT: cross-vehicle perception for vision-based cooperative driving
ππ’π π‘π₯π’π π‘ππ¬:
β UTexas + #Stanford + #Sony #AI
β LiDAR into compact point-based
β Network-augmented simulator
β Source code and models available
More: https://bit.ly/3sr5HLk
πCOOPERNAUT: cross-vehicle perception for vision-based cooperative driving
ππ’π π‘π₯π’π π‘ππ¬:
β UTexas + #Stanford + #Sony #AI
β LiDAR into compact point-based
β Network-augmented simulator
β Source code and models available
More: https://bit.ly/3sr5HLk
π₯6π€―3π₯°1
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πNeuralHDHair: 3D Neural Hairπ
πNeuralHDHair: fully automatic system for modeling HD hair from a single image
ππ’π π‘π₯π’π π‘ππ¬:
β IRHairNet for hair geometric features
β GrowingNet: 3D hair strands in parallel
β VIFu: novel voxel-aligned implicit function
β SOTA in 3D hair modeling from single pic
More: https://bit.ly/38iR0mQ
πNeuralHDHair: fully automatic system for modeling HD hair from a single image
ππ’π π‘π₯π’π π‘ππ¬:
β IRHairNet for hair geometric features
β GrowingNet: 3D hair strands in parallel
β VIFu: novel voxel-aligned implicit function
β SOTA in 3D hair modeling from single pic
More: https://bit.ly/38iR0mQ
π5π₯°3β€1
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π‘DyNeRF: Neural 3D Video Synthesisπ‘
π#Meta unveils DyNeRF, novel rendering HQ 3D video
ππ’π π‘π₯π’π π‘ππ¬:
β Novel NeRF-based on temp-latent codes
β Novel training based on hierarchical step
β Datasets of time-synch/calibrated clips
β Attribution-NonCommercial 4.0 Int.
More: https://bit.ly/3MlBRA9
π#Meta unveils DyNeRF, novel rendering HQ 3D video
ππ’π π‘π₯π’π π‘ππ¬:
β Novel NeRF-based on temp-latent codes
β Novel training based on hierarchical step
β Datasets of time-synch/calibrated clips
β Attribution-NonCommercial 4.0 Int.
More: https://bit.ly/3MlBRA9
π€―8π2π₯1π€©1
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πGATO: agent for multiple tasksπ
πThe same network with the same weights can play Atari, caption pics, chat, and moreπ€―
ππ’π π‘π₯π’π π‘ππ¬:
β General-purpose agent, multiple tasks
β Multi-modal-task, multi-embodiment
β Inspired by large-scale language model
More: https://bit.ly/3LbBOWb
πThe same network with the same weights can play Atari, caption pics, chat, and moreπ€―
ππ’π π‘π₯π’π π‘ππ¬:
β General-purpose agent, multiple tasks
β Multi-modal-task, multi-embodiment
β Inspired by large-scale language model
More: https://bit.ly/3LbBOWb
π€―10β€3π2π₯2
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πͺNeRF powered by keypointsπͺ
πETHZ + META unveil how to encode relative spatial #3D info via sparse 3D keypoints
ππ’π π‘π₯π’π π‘ππ¬:
β Sparse 3D keypoints for SOTA avatars
β Unseen subjects from 2/3 views
β Never-before-seen iPhone captures
More: https://bit.ly/39NQqhe
πETHZ + META unveil how to encode relative spatial #3D info via sparse 3D keypoints
ππ’π π‘π₯π’π π‘ππ¬:
β Sparse 3D keypoints for SOTA avatars
β Unseen subjects from 2/3 views
β Never-before-seen iPhone captures
More: https://bit.ly/39NQqhe
π€―5π₯2β€1π1
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πSelf-Supervised human co-evolutionπ
πSelf-supervised 3D by co-evolution of pose estimator, imitator, and hallucinator
ππ’π π‘π₯π’π π‘ππ¬:
β Novel self-supervised 3D pose
β Co-evo of pose, imitator, hallucinator
β Realist 3D pose and 2D-3D supervision
β Source code / model under MIT license
More: https://bit.ly/37J5ImL
πSelf-supervised 3D by co-evolution of pose estimator, imitator, and hallucinator
ππ’π π‘π₯π’π π‘ππ¬:
β Novel self-supervised 3D pose
β Co-evo of pose, imitator, hallucinator
β Realist 3D pose and 2D-3D supervision
β Source code / model under MIT license
More: https://bit.ly/37J5ImL
π₯4π3β€1π€―1
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π² Diff-SDF #3D Rendering π²
πReconstruction with no complex reg. or priors, using only a per-pixel RGB loss
ππ’π π‘π₯π’π π‘ππ¬:
β Diff-render to optimize geometry/albedo
β No ad-hoc object mask or supervision
β Extended sphere tracing algorithm
More: https://bit.ly/3yKWPnI
πReconstruction with no complex reg. or priors, using only a per-pixel RGB loss
ππ’π π‘π₯π’π π‘ππ¬:
β Diff-render to optimize geometry/albedo
β No ad-hoc object mask or supervision
β Extended sphere tracing algorithm
More: https://bit.ly/3yKWPnI
π€―10π4π₯2β€1π€©1
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πLVD: new SOTA for #3D humanπ
πCorona et al. unveils a novel 3D human model fitting
ππ’π π‘π₯π’π π‘ππ¬:
β Solution via neural field
β Not sensitive to initialization
β SOTA in shape from single pic
β SOTA in fitting 3D scans
More: https://bit.ly/3Ng4lLr
πCorona et al. unveils a novel 3D human model fitting
ππ’π π‘π₯π’π π‘ππ¬:
β Solution via neural field
β Not sensitive to initialization
β SOTA in shape from single pic
β SOTA in fitting 3D scans
More: https://bit.ly/3Ng4lLr
π4π₯2π€―1
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π³οΈβπDeep Clustering on ImageNet & Co.π³οΈβπ
πWorld's first deep nonparametric clustering on large dataset such as ImageNet
ππ’π π‘π₯π’π π‘ππ¬:
β Deep clustering that infers nr. of clusters
β Loss: amortized inference in mixt-models
β Deep nonparametric clustering on ImageNet
β Code and model available under MIT license
More: https://bit.ly/38p62rn
πWorld's first deep nonparametric clustering on large dataset such as ImageNet
ππ’π π‘π₯π’π π‘ππ¬:
β Deep clustering that infers nr. of clusters
β Loss: amortized inference in mixt-models
β Deep nonparametric clustering on ImageNet
β Code and model available under MIT license
More: https://bit.ly/38p62rn
π₯9π€―3π2π€©2
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π₯HQ-EΒ²FGVI just releasedπ₯π₯
πFlow-Guided Video Inpainting through three trainable modules
ππ’π π‘π₯π’π π‘ππ¬:
β Flow, pixel-prop, content hallucination
β Three stage-modules, jointly optimized
β The new SOTA, promising efficiency
β Code and Models under MIT license
More: https://bit.ly/3Ln0ICj
πFlow-Guided Video Inpainting through three trainable modules
ππ’π π‘π₯π’π π‘ππ¬:
β Flow, pixel-prop, content hallucination
β Three stage-modules, jointly optimized
β The new SOTA, promising efficiency
β Code and Models under MIT license
More: https://bit.ly/3Ln0ICj
π€―10π1π±1
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πͺ AvatarCLIP: Text-Driven Avatar πͺ
πZero-shot text-driven for #3D avatar in #metaverse
ππ’π π‘π₯π’π π‘ππ¬:
β First text-driven synthesis
β Shape, texture, and motion
β Animation-ready, HQ texture/geometry
β Zero-shot text-guided ref-based motion
β Code and model under MIT license
More: https://bit.ly/3LjTWgB
πZero-shot text-driven for #3D avatar in #metaverse
ππ’π π‘π₯π’π π‘ππ¬:
β First text-driven synthesis
β Shape, texture, and motion
β Animation-ready, HQ texture/geometry
β Zero-shot text-guided ref-based motion
β Code and model under MIT license
More: https://bit.ly/3LjTWgB
π₯4π2π€―2β€1
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π₯#AIwithPapers: we are 2,500!π₯
ππOnly 2 Billion papers remaining on arXiv. The more we are, the faster we readππ
π Invite your friends -> https://t.me/AI_DeepLearning
ππOnly 2 Billion papers remaining on arXiv. The more we are, the faster we readππ
π Invite your friends -> https://t.me/AI_DeepLearning
π₯9β€4π2π€2π1
π₯Podcasting AI & CVπ₯
ππΌFor people fluent in Italian: 1 hour podcast in which I talk about AI, CV, Startup and more (included this wonderful project).
More: https://bit.ly/38DtBwB
ππΌFor people fluent in Italian: 1 hour podcast in which I talk about AI, CV, Startup and more (included this wonderful project).
More: https://bit.ly/38DtBwB
π6β€3π1
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π₯Inpainting: new SOTA! INSANEπ₯
πNovel two-stream approach: inpainting at the next level!
ππ’π π‘π₯π’π π‘ππ¬:
β High-freq locally, low-freq globally
β Local to global -> error correction
β 44% / 26% improvements FID/scores
β Source code, more clips available
More: https://bit.ly/3ltIX9R
πNovel two-stream approach: inpainting at the next level!
ππ’π π‘π₯π’π π‘ππ¬:
β High-freq locally, low-freq globally
β Local to global -> error correction
β 44% / 26% improvements FID/scores
β Source code, more clips available
More: https://bit.ly/3ltIX9R
π8π€―3π₯1π₯°1
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π₯Super-Human Crossword Solverπ₯
πSolving crosswords outperforming best humans
ππ’π π‘π₯π’π π‘ππ¬:
β Crossword solving based on NNs
β Q&A, structured decoding, local search
β Wide domains with perfect accuracy
β Large question-answer dataset
More: https://bit.ly/3a3zzqQ
πSolving crosswords outperforming best humans
ππ’π π‘π₯π’π π‘ππ¬:
β Crossword solving based on NNs
β Q&A, structured decoding, local search
β Wide domains with perfect accuracy
β Large question-answer dataset
More: https://bit.ly/3a3zzqQ
π₯4π€―3π2π1
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π₯ΈImagen: far beyond DALLΒ·E 2π₯Έ
π#Google: unprecedented photorealism and deep level of language understanding
ππ’π π‘π₯π’π π‘ππ¬:
β Dynamic thresh diffusion sampling
β Efficient U-Net, efficient++ variant
β DrawBench, new text-to-image
β The new SOTA, COCO FID of 7.27
More: https://bit.ly/3lVtkbz
π#Google: unprecedented photorealism and deep level of language understanding
ππ’π π‘π₯π’π π‘ππ¬:
β Dynamic thresh diffusion sampling
β Efficient U-Net, efficient++ variant
β DrawBench, new text-to-image
β The new SOTA, COCO FID of 7.27
More: https://bit.ly/3lVtkbz
π₯9π€―6π1
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πͺ€Tracking over SOTA detectorsπͺ€
πLightweight Python lib for real-time 2D object tracking π₯
ππ’π π‘π₯π’π π‘ππ¬:
β Layer of tracking over SOTA detectors
β Suitable for complex video processing
β Source code under BSD 3-Clause
β Maintained by Tryolabs team
More: https://bit.ly/3wKtGqg
πLightweight Python lib for real-time 2D object tracking π₯
ππ’π π‘π₯π’π π‘ππ¬:
β Layer of tracking over SOTA detectors
β Suitable for complex video processing
β Source code under BSD 3-Clause
β Maintained by Tryolabs team
More: https://bit.ly/3wKtGqg
π7π₯3π€©3