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🥦 GAN-generated CryptoPunks 🥦
👉A simple (and funny) SN-GAN to generate cryptopunks
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Spectral normalization (2018)
✅Easy to incorporate into training
✅A project by Teddy Koker 🎩
More: https://bit.ly/35C1rQI
👉A simple (and funny) SN-GAN to generate cryptopunks
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Spectral normalization (2018)
✅Easy to incorporate into training
✅A project by Teddy Koker 🎩
More: https://bit.ly/35C1rQI
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🤪SEER: self-AI from BILLIONS pic🤪
👉META + INRIA trained models on billions of random images without any pre-processing or assumptions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Self-supervised on pics from web
✅Discovering properties in datasets
✅More fair, less biased & less harmful
✅Better OOD generalization
✅Source code available!
More: https://bit.ly/3vy69dd
👉META + INRIA trained models on billions of random images without any pre-processing or assumptions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Self-supervised on pics from web
✅Discovering properties in datasets
✅More fair, less biased & less harmful
✅Better OOD generalization
✅Source code available!
More: https://bit.ly/3vy69dd
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🐲A novel AI-controllable synthesis🐲
👉Modeling local semantic parts separately and synthesizing images in a compositional way
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Structure & texture locally controlled
✅Disentanglement between areas
✅Fine-grained editing of images
✅Extendible via transfer learning
✅Just accepted to #CVPR2022
More: https://bit.ly/3IBgkBy
👉Modeling local semantic parts separately and synthesizing images in a compositional way
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Structure & texture locally controlled
✅Disentanglement between areas
✅Fine-grained editing of images
✅Extendible via transfer learning
✅Just accepted to #CVPR2022
More: https://bit.ly/3IBgkBy
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🥣 #AI-Generation with Dream Fields 🥣
👉Neural rendering with multi-modal image and text representations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Aligned image & text models
✅3D from natural language
✅No additional data
✅D.F. neural-scene
More: https://bit.ly/3Mhwm5D
👉Neural rendering with multi-modal image and text representations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Aligned image & text models
✅3D from natural language
✅No additional data
✅D.F. neural-scene
More: https://bit.ly/3Mhwm5D
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🟪 Mip-NeRF 360 for unbounded scenes 🟪
👉An extension of NeRF to overcome the challenges presented by unbounded scenes
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Realistic synthesized views
✅Intricate/unbounded scenes
✅Detailed depth maps
✅Mean-squared error -54%
✅No code provided 😥
More: https://bit.ly/36ZxsD4
👉An extension of NeRF to overcome the challenges presented by unbounded scenes
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Realistic synthesized views
✅Intricate/unbounded scenes
✅Detailed depth maps
✅Mean-squared error -54%
✅No code provided 😥
More: https://bit.ly/36ZxsD4
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🐓 PINA: personal Neural Avatar 🐓
👉A novel method to acquire neural avatars from RGB-D videos
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅A virtual copy of themselves
✅Realistic clothing deformations
✅Shape & non-rigid deformation
✅Avatars from RGB-D sequences
✅Creative Commons Zero v1.0
More: https://bit.ly/3HAtRIh
👉A novel method to acquire neural avatars from RGB-D videos
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅A virtual copy of themselves
✅Realistic clothing deformations
✅Shape & non-rigid deformation
✅Avatars from RGB-D sequences
✅Creative Commons Zero v1.0
More: https://bit.ly/3HAtRIh
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🐦 EfficientVIS: new SOTA for VIS 🐦
👉Simultaneous classification, segmentation, and tracking multiple object instances in videos
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Efficient and fully end-to-end
✅Iterative query-video interaction
✅First RoI-wise clip-level RT-VIS
✅Requires 15× fewer epochs
More: https://bit.ly/3KfqurN
👉Simultaneous classification, segmentation, and tracking multiple object instances in videos
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Efficient and fully end-to-end
✅Iterative query-video interaction
✅First RoI-wise clip-level RT-VIS
✅Requires 15× fewer epochs
More: https://bit.ly/3KfqurN
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🐠#AI-clips from single frame🐠
👉Moving objects in #3D while generating a video by a sequence of desired actions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅A playable environments
✅A single starting image🤯
✅Controllable camera
✅Unsupervised learning
More: https://bit.ly/35VDrYO
👉Moving objects in #3D while generating a video by a sequence of desired actions
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅A playable environments
✅A single starting image🤯
✅Controllable camera
✅Unsupervised learning
More: https://bit.ly/35VDrYO
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🧊Kubric: AI dataset generator🧊
👉Open-source #Python framework for photo-realistic scenes: full control, rich annotations, TBs of fresh data 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Synthetic datasets with GT
✅From NeRF to optical flow
✅Full control over data
✅Ok privacy & licensing
✅Apache License 2.0
More: https://bit.ly/3hQCaFs
👉Open-source #Python framework for photo-realistic scenes: full control, rich annotations, TBs of fresh data 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Synthetic datasets with GT
✅From NeRF to optical flow
✅Full control over data
✅Ok privacy & licensing
✅Apache License 2.0
More: https://bit.ly/3hQCaFs
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🪂µTransfer for enormous NNs 🪂
👉Microsoft unveils how to tune enormous neural networks
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅New HP tuning: µTransfer
✅Zero-shot transfer to full-model
✅Outperforming BERT-large
✅Outperforming 6.7B GPT-3
✅Code under MIT license
More: https://bit.ly/3qc37Ij
👉Microsoft unveils how to tune enormous neural networks
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅New HP tuning: µTransfer
✅Zero-shot transfer to full-model
✅Outperforming BERT-large
✅Outperforming 6.7B GPT-3
✅Code under MIT license
More: https://bit.ly/3qc37Ij
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🐧Semantic via only text supervision🐧
👉GroupViT with a text encoder on a large-scale image-text dataset: semantic with any pixel-level annotations in training!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hierarc. Grouping Vision Transf.
✅Additional text encoder
✅NO pixel-level annotations
✅Semantic-seg task via zero-shot
✅Source code available soon
More:https://bit.ly/3hPGeWr
👉GroupViT with a text encoder on a large-scale image-text dataset: semantic with any pixel-level annotations in training!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hierarc. Grouping Vision Transf.
✅Additional text encoder
✅NO pixel-level annotations
✅Semantic-seg task via zero-shot
✅Source code available soon
More:https://bit.ly/3hPGeWr
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⌚4D-Net: Lidar + RGB synchronization⌚
👉Google unveils 4D-Net to combine 3D LiDAR and onboard RGB camera
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Point clouds/images in time
✅Fusing multiple modalities in 4D
✅Novel sampling for 3D P.C. in time
✅New SOTA for 3D detection
More: https://bit.ly/3hZCFwN
👉Google unveils 4D-Net to combine 3D LiDAR and onboard RGB camera
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Point clouds/images in time
✅Fusing multiple modalities in 4D
✅Novel sampling for 3D P.C. in time
✅New SOTA for 3D detection
More: https://bit.ly/3hZCFwN
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🐌 New SOTA in video synthesis! 🐌
👉Snap unveils a novel multimodal video generation framework via text/images
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Multimodal video generation
✅Bidirectional transformer
✅Video token with self-learn.
✅Text augmentation for robustness
✅Longer sequence synthesis
More: https://bit.ly/3hZLXsG
👉Snap unveils a novel multimodal video generation framework via text/images
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Multimodal video generation
✅Bidirectional transformer
✅Video token with self-learn.
✅Text augmentation for robustness
✅Longer sequence synthesis
More: https://bit.ly/3hZLXsG
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🎁 StyelNeRF source code is out 🎁
👉3D consistent photo-realistic image synthesis
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF + style generator
✅3D consistency for HD image
✅Novel regularization loss
✅Camera control on styles
More: https://bit.ly/3t5xC49
👉3D consistent photo-realistic image synthesis
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅NeRF + style generator
✅3D consistency for HD image
✅Novel regularization loss
✅Camera control on styles
More: https://bit.ly/3t5xC49
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🦎CLD-based generative #AI by #Nvidia🦎
👉Nvidia unveils a novel critically-damped Langevin diffusion (CLD) for synthetic data
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅A novel diffusion process for SGMs
✅Novel score matching obj. for CLD
✅Hybrid denoising score matching
✅Efficient sampling from CLD model
✅Source code under a specific license
More: https://bit.ly/35MToBe
👉Nvidia unveils a novel critically-damped Langevin diffusion (CLD) for synthetic data
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅A novel diffusion process for SGMs
✅Novel score matching obj. for CLD
✅Hybrid denoising score matching
✅Efficient sampling from CLD model
✅Source code under a specific license
More: https://bit.ly/35MToBe
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🛸UFO: segmentation @140+ FPS🛸
👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unified framework for co-segmentation
✅Co-segmentation, co-saliency, saliency
✅Block for long-range dependencies
✅Able to reach for 140 FPS in inference
✅The new SOTA on multiple datasets
✅Source code under MIT License
More: https://bit.ly/3KLd9b9
👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unified framework for co-segmentation
✅Co-segmentation, co-saliency, saliency
✅Block for long-range dependencies
✅Able to reach for 140 FPS in inference
✅The new SOTA on multiple datasets
✅Source code under MIT License
More: https://bit.ly/3KLd9b9
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👗 Multi-GANs fashion 👗
👉Global GAN blended with other GANs for faces, shoes, etc.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Multi-GAN framework
✅Several generators
✅Free of artifacts
✅Full-body generation
✅Humans, 1024x1024
More: https://bit.ly/37mfOte
👉Global GAN blended with other GANs for faces, shoes, etc.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Multi-GAN framework
✅Several generators
✅Free of artifacts
✅Full-body generation
✅Humans, 1024x1024
More: https://bit.ly/37mfOte
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🚧 FLAG: #3D Avatar Generation 🚧
👉A flow-based generative model of the 3D human body from sparse observations.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅FLow-based Avatar Generative
✅Conditional distro of body pose
✅Exact pose likelihood process
✅Invertibility -> oracle latent code
More: https://bit.ly/3CQpk3p
👉A flow-based generative model of the 3D human body from sparse observations.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅FLow-based Avatar Generative
✅Conditional distro of body pose
✅Exact pose likelihood process
✅Invertibility -> oracle latent code
More: https://bit.ly/3CQpk3p
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💃 Dancing in the wild with StyleGAN 💃
👉StyleGAN-based animations for AR/VR apps
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Video based motion retargeting
✅A StyleGAN architecture based
✅Novel explicit motion representation
✅SOTA qualitatively & quantitatively
More: https://bit.ly/3CZbL1W
👉StyleGAN-based animations for AR/VR apps
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Video based motion retargeting
✅A StyleGAN architecture based
✅Novel explicit motion representation
✅SOTA qualitatively & quantitatively
More: https://bit.ly/3CZbL1W
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🪀TensoRF: the 4D evolution of NeRF 🪀
👉TensoRF, a novel radiance fields via 4D-tensor: 3D voxel grid with per-voxel multi-channel feats.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅VM decomposition technique
✅Low-rank tensor factorization
✅Lower memory footprint (speed)
✅TensoRF is the new SOTA in R.F.
✅Code under the MIT License
More: https://bit.ly/3qffZgI
👉TensoRF, a novel radiance fields via 4D-tensor: 3D voxel grid with per-voxel multi-channel feats.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅VM decomposition technique
✅Low-rank tensor factorization
✅Lower memory footprint (speed)
✅TensoRF is the new SOTA in R.F.
✅Code under the MIT License
More: https://bit.ly/3qffZgI
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🔼 GAN-meshes without key-points 🔼
👉ETH unveils a GAN framework for generating textured triangle meshes without annotations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Generative of textured meshes
✅3D generator for all categories
✅3D pose estimation framework
✅Code licensed under MIT License
More: https://bit.ly/3qfH9nJ
👉ETH unveils a GAN framework for generating textured triangle meshes without annotations
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Generative of textured meshes
✅3D generator for all categories
✅3D pose estimation framework
✅Code licensed under MIT License
More: https://bit.ly/3qfH9nJ
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