🔋V2X-sim for #selfdriving is out!🔋

👉V2X: collaboration between a vehicle and any surrounding entity

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Suitable for #selfdrivingcars
✅Rec. from road & vehicles
✅Multi-streams/perception
✅Detection, tracking, & segmentation
✅RGB, depth, semantic, BEV & LiDAR

More: https://bit.ly/3H6veOI

🍏Infinite Synthetic dataset for Fitness🍏

👉Opensource synthetic images for fitness, single/multi-person, and realistic variation in lighting, camera angles, and occlusions

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅60k images, 1-5 avatars
✅15 categories, 21 variations
✅Blender and ray-tracing
✅SMPL-X + facial expression
✅Cloth/skin tone sampled
✅147 4K HDRI panoramas
✅Creative Commons 4.0

More: https://bit.ly/33B1R9q

♊ DITTO: Digital Twins from Interaction ♊

👉Digitizing objects for #metaverse through interactive perception

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅DIgital Twin of arTiculated Objects
✅Geometry & kinematic articulation
✅Articulation & 3D via perception
✅Source code under MIT License

More:https://bit.ly/3LMazCV

🤖 Robotic Telekinesis from Youtube 🤖

👉CMU unveils a Robot that observes humans and imitates their actions in real-time

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Enabling robo-hand teleoperation
✅Suitable for untrained operator
✅Single uncalibrated RGB camera
✅Leveraging unlabeled #youtube
✅No active fine-tuning or setup
✅No collision via Adv-Training

More: https://bit.ly/3H7zUnh

💄DIGAN: #AI for video generation💄

👉A novel INR-based generative adversarial network for video generation

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Dynamics-aware generator
✅INR-based clip generator
✅Manipulating space/time
✅Identifying unnatural motion

More: https://bit.ly/3H6sHE4

🦄FILM Neural Frame Interpolation🦄

👉Frame interpolation that synthesizes multiple intermediate frames from two input images with large in-between motion

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Single unified network
✅High quality output
✅SOTA on the Xiph
✅Apache License 2.0

More: https://bit.ly/3pl4ZxH

🔈Neural Maintenance via listening🔈

👉Novel neural-method to detect whether a machine is "healthy" or requires maintenance

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Defects at an early stage
✅FDWT, fast discrete wavelet
✅Learnable wavelet/denoising
✅Unsupervised learnable FDWT
✅The new SOTA in PM

More: https://bit.ly/3hiKWeX

🟦🟨 StyleGAN on Internet pics 🟦🟨

👉StyleGAN on raw uncurated images collected from Internet

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Outliers & multi-modal
✅Self-distillation approach
✅Self-filtering of outliers
✅Perceptual clustering

More: https://bit.ly/33Z1d5H

🦜The new SOTA for Unsupervised 🦜

👉Self-supervised transformer to discover objects in images

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Visual tokens as nodes in graph
✅Edges as connectivity score
✅The second smallest eV = fg
✅Suitable for unsupervised saliency
✅Weakly supervised obj. detection
✅Code under MIT License


More: https://bit.ly/3sqbFg3

🥦 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

🤪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

🐲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

🥣 #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

🟪 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

🐓 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

🐦 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

🐠#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

🧊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

🪂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

🐧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