🐍DS Unsupervised Video Decomposition🐍

👉Novel method to extract persistent elements of a scene

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Scene element as Deformable Sprite (DS)
✅Deformable Sprites by video auto-encoder
✅Canonical texture image for appearance
✅Non-rigid geom. transformation

More: https://bit.ly/37WV9w1

🥓 L-SVPE for Deep Deblurring 🥓

👉L-SVPE to deblur scenes while recovering high-freq details

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Learned Spatially Varying Pixel Exposures
✅Next-gen focal-plane sensor + DL
✅Deep conv decoder for motion deblurring
✅Superior results over non-optimized exp.

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

🧧Hyper-Fast Instance Segmentation🧧

👉Novel Temporally Efficient Vision Transformer (TeViT) for VIS

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Video instance segmentation transformer
✅Contextual-info at frame/instance level
✅Nearly convolution-free framework 🤷‍♂️
✅The new SOTA for VIS, ~70 FPS!
✅Code & models under MIT license

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

📗Unified Scene Text/Layout Detection📗

👉World's first hierarchical scene text dataset + novel detection method

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unified detection & geometric layout
✅Hierarchical annotations in natural scenes
✅Word, line, & paragraph level annotations
✅Source under CC Attribution Share Alike 4.0

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

🙌 #Oculus' new Hand Tracking 🙌

👉Hands are able to move as naturally and intuitively in the #metaverse as do in real life

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hands2.0 powered by CV & ML
✅Tracking hand-over-hand interactions
✅Crossing hands, clapping, high-fives
✅Accurate thumbs-up gesture

More: https://bit.ly/3JXPvY2

🎗️New SOTA in #3D human avatar🎗️

👉PHORHUM: photorealistic 3D human from mono-RGB

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Pixel-aligned method for 3D geometry
✅Unshaded surface color + illumination
✅Patch-based rendering losses for visible
✅Plausible color estimation for non-visible

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

📟 What's in your hands (#3D) ? 📟

👉Reconstructing hand-held objects (from single RGB) without knowing their 3D templates🤷‍♂️

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hand is highly predictive of object shape
✅Conditional-based on the articulation
✅Visual feats. / articulation-aware coords.
✅Code and models available!

More: https://bit.ly/3vuYn2a

🔋YODO: You Only Demonstrate Once🔋

👉A novel category-level manipulation learned in sim from single demonstration video🤯

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅One-shot IL, model-free 6D pose tracking
✅Demonstration BY single 3rd-person-view
✅manipulation including hi-precision tasks
✅Category-level Behavior Cloning
✅Attention for dynamic coords selection
✅Generalizability to novel unseen obj/env

More: https://bit.ly/3v0V4R4

👗 Dress Code for Virtual Try-On 👗

👉UniMORE (+ YOOX) unveils a novel dataset/approach for virtual try-on.

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hi-Res paired front-view / full-body
✅Pixel-level Semantic-Aware Discriminator
✅9 SOTA VTON approaches / 3 baselines
✅New SOTA considering res. & garments

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

🍃Deep Equilibrium for Optical Flow🍃

👉DEQ: converge faster, less memory, often more accurate

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel formulation of optical flow method
✅Compatible with prior modeling/data-related
✅Sparse fixed-point correction for stability
✅Code/models under GNU Affero GPL v3.0

More: https://bit.ly/3v4fZmi

🌳Ultra High-Resolution Neural Saliency🌳

👉A novel ultra high-resolution saliency detector with dataset!

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Ultra Hi-Res Saliency Detection
✅5,920 pics at 4K-8K resolution
✅Pyramid Grafting Network
✅Cross-Model Grafting Module
✅AGL: Attention Guided Loss
✅Code/models under MIT

More: https://bit.ly/3MnU1Rf

🪆StyleGAN-Human for fashion 🪆

👉A novel unconditional human generation based on StyleGAN is out!

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅200,000+ labeled sample (pose/texture)
✅1024x512 StyleGAN-Human StyleGAN3
✅512x256 StyleGAN-Human StyleGAN1
✅Face model for downstream: InsetGAN
✅Source code and model available!

More: https://bit.ly/3xMg5B2

💀 OSSO: Skeletal Shape from Outside 💀

👉Anatomic skeleton of a person from 3D surface of body 🦴

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Max Planck + IMATI-CNR + INRIA
✅DXA images to obtain #3D shape
✅External body to internal skeleton

More: https://bit.ly/3v7Z5TQ

🎷 Pix2Seq: object detection by #Google 🎷

👉A novel framework to perform object detection as a language modeling task

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Obj. detection as a lang-modeling task
✅BBs/labels -> seq. of discrete token
✅Encoder-decoder (one token at a time)
✅Code under Apache License 2.0

More: https://bit.ly/3F49PX3

🌹 Generalizable Neural Performer 🌹

👉General neural framework to synthesize free-viewpoint images of arbitrary human performers

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Free-viewpoint synthesis of humans
✅Implicit Geometric Body Embedding
✅Screen-Space Occlusion-Aware Blending
✅GeneBody: 4M frames, multi-view cams

More: https://cutt.ly/SGcnQzn

🚌 Tire-defect inspection 🚌

👉Unsupervised defects in tires using neural networks

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Impurity, same material as tire
✅Impurity, with different material
✅Damage by temp/pressure
✅Crack or etched material

More: https://bit.ly/37GX1JT

🧋#4D Neural Fields🧋

👉4D N.F. visual representations from monocular RGB-D 🤯

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅4D scene completion (occlusions)
✅Scene completion in cluttered scenes
✅Novel #AI for contextual point clouds
✅Data, code, models under MIT license

More: https://cutt.ly/6GveKiJ

👔Largest dataset of human-object 👔

👉BEHAVE by Google: largest dataset of human-object interactions

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅8 subjects, 20 objects, 5 envs.
✅321 clips with 4 Kinect RGB-D
✅Masks and segmented point clouds
✅3D SMPL & mesh registration
✅Textured scan reconstructions

More: https://bit.ly/3Lx6NNo

🦴ENARF-GAN Neural Articulations🦴

👉Unsupervised method for 3D geometry-aware representation of articulated objects

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel efficient neural representation
✅Tri-planes deformation fields for training
✅Novel GAN for articulated representations
✅Controllable 3D from real unlabeled pic

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

🖲️ HuMMan: 4D human dataset 🖲️

👉HuMMan: 4D dataset with 1000 humans, 400k sequences & 60M frames 🤯

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅RGB, pt-clouds, keypts, SMPL, texture
✅Mobile device in the sensor suite
✅500+ actions to cover movements

More: https://bit.ly/3vTRW8Z

🔥Neighborhood Attention Transformer 🔥

👉A novel transformer for both image classification and downstream vision tasks

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Neighborhood Attention (NA)
✅Neighborhood Attention Transformer, NAT
✅Faster training/inference, good throughput
✅Checkpoints, train, #CUDA kernel available

More: https://bit.ly/3F5aVSo