🦒 Text2LIVE: Text-Driven Neural Editing 🦒

👉#Amazon unveils a novel #AI for text-driven edit of videos. Insane! 🤯

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Semantic edits of real-world videos
✅Edit layer–RGBA representing target
✅Edit layers synthesized on single input
✅No masks or a pre-trained generator

More: https://bit.ly/3NVP6aE

📟📟AI-Designed Circuits with Deep RL📟📟

👉#Nvidia unveils an #AI to design circuits from scratch, smaller and faster than SOTA ones

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Parallel prefix circuits for Hi-Perf
✅RL framework to explore the circuit space
✅Smaller, Faster, Power-- from the scratch

More: https://bit.ly/3yY9dk7

👽 Neural I2I with a few shoots 👽

👉#Alibaba unveils a novel portrait stylization. Limited samples (∼100) -> HD outputs

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Calibration first, translation later
✅Balanced distribution to calibrate bias
✅Spatially semantic constraints via geometry
✅Source code and models soon available!

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

🤹‍♂️ K-Means Mask Transformer 🤹‍♂️

👉#Google AI unveils kMaX-DeepLab, novel E2E method for segmentation

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅kMaX-DeepLab: k-means Mask Xformer
✅Rethinking relationship pixels / object
✅Cross-attention -> k-means clustering
✅The new SOTA on several dataset

More: https://bit.ly/3O2QV5I

☀️ 4D Neural Relightable Humans ☀️

👉Relighting4D: free-viewpoints relighting of humans under unknown illuminations

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Relight dynamic, free viewpoints
✅Disentangled reflectance/geometry
✅SOTA on synthetic/real datasets
✅Code/models under MIT License

More: https://bit.ly/3RF3yH9

🍰 Long-Term Object Segmentation 🍰

👉XMem: object segmentation for long clips with unified feature memory stores

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Inspired by Atkinson–Shiffrin model
✅Stores with different temporal scales
✅Memory consolidation algorithm
✅Compact/powerful long-term memory
✅Source code and models available

More: https://bit.ly/3PP0EOn

🔥🔥 Update 🔥🔥

👉Code https://github.com/THUDM/CogVideo

👉Demo https://wudao.aminer.cn/cogvideo/

More: https://bit.ly/3yP86BQ

🔥Grand Unification of Object Tracking🔥

👉UNICORN: unified method for SOT, MOT, VOS, & MOTS with a single neural net. 🤯

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Great unification for 4 tracking tasks
✅Bridging methods / pixel-wise corresp.
✅SOTA on 8 challenging benchmarks
✅Source code under MIT License

More: https://bit.ly/3o74h6g

🔥OmniBenchmark: CV beyond ImageNet🔥

👉 21 realms, 7,000+ concepts and 1M+ images. Far beyond ImageNet!

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅vs. ImageNet: 2.5x realms, 9x concepts
✅Conciseness: no concept overlapping
✅ReCo: Relational Contrastive Learning
✅New supervised contrastive learning SOTA

More: https://bit.ly/3RJRKU0

💣 HD Neural Avatar @130FPS 💣

👉Samsung unveils MegaPortraits: novel one-shot creation of HD neural human avatar

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅One-shot neural avatars, SOTA up 512p
✅"Upgrading" to megapixel via more pics
✅First Neural Head Avatars in HD
✅Up to to 130 FPS via #GPU

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

🔥Important update from #OpenAI🔥

👉 https://openai.com/blog/reducing-bias-and-improving-safety-in-dall-e-2/

🦚 TimeLens++: Event-based Interpolation 🦚

👉Novel event-based interpolation with non-linear flow & multi-scale fusion

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel motion spline estimator
✅Non-linear continuous event/frames flow
✅Multi-feature fusion, gated compression
✅Novel hybrid dataset with 100+ videos

More: https://bit.ly/3yJyY6g

🪰NUWA-Infinity is out!🪰

👉∞ generation by #Microsoft: arbitrarily-sized HD images and long videos 🤯

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unconditional Image Gen.
✅Text-to-Image/Text-to-Clip
✅Animation / Out-painting
✅Hi-res, arbitrary long clip
✅NCP for patches caching

More: https://bit.ly/3zmBf9f

🔥 #AIwithPapers: we are 3,500+! 🔥

💙💛 Ready for YOLO 10, 11, π, ∞, Ψ, and more? The more we are, the faster we catch'em all 💙💛

😈 Invite your friends -> https://t.me/AI_DeepLearning

🎷🎷OMNI3D: #3D Objects in the Wild🎷🎷

👉#3D detection: 234k images, 3M+ instances & 97 categories

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅OMNI3D from publicly released dataset
✅234k pics, 3M+ annotation with 3D box
✅97 categories such as sofa, table, cars
✅Fast (450x) and exact algorithm for IoU
✅Cube R-CNN: novel 3D object detector

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

👹Multiface Neural Rendering 👹

👉A new multi-view, Hi-Res data collected at #META Reality Labs for neural face

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Mugsy, large scale multi-cam apparatus
✅High-Res sync facial performance
✅Closing the gap in accessing HQ data
✅Suitable for #VR & #mixedreality

More: https://bit.ly/3b6XfeL

💄DEVIANT: SOTA in mono-3D detection💄

👉A novel Depth EquiVarIAnt NeTwork for 3D monocular detection in the wild

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Michigan + #Meta + Ford 🤯
✅Depth-equi. + scale equiv. steerable
✅New SOTA on KITTI & Waymo
✅Ok cross-dataset -> generalization

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

🧱 Assembling #LEGO with #AI 🧱

👉Step-by-step assembly manual created by human into machine-interpretable instructions

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Stanford + MIT + #Google 🤯
✅MEPNet: Manual-to-Executable-Plan Net
✅Manual to machine-executable plan
✅2D manual - 3D geometric shape
✅Reasoning on 3D alignments of legos

More: https://bit.ly/3PCwn5C

🔥🔥 UPDATE 🔥🔥

Code Released: https://github.com/apple/ml-mobileone