💄TinyBeauty: 460 FPS Make-up💄

👉TinyBeauty: only 80K parameters to achieve the SOTA in virtual makeup without intricate face prompts. Up to 460 FPS on mobile!

👉Review https://t.ly/LG5ok
👉Paper https://arxiv.org/pdf/2403.15033.pdf
👉Project https://tinybeauty.github.io/TinyBeauty/

☔ AiOS: All-in-One-Stage Humans ☔

👉All-in-one-stage framework for SOTA multiple expressive pose and shape recovery without additional human detection step.

👉Review https://t.ly/ekNd4
👉Paper https://arxiv.org/pdf/2403.17934.pdf
👉Project https://ttxskk.github.io/AiOS/
👉Code/Demo (announced)

🏀 MAVOS Object Segmentation 🏀

👉MAVOS is a transformer-based VOS w/ a novel, optimized and dynamic long-term modulated cross-attention memory. Code & Models announced (BSD 3-Clause)💙

👉Review https://t.ly/SKaRG
👉Paper https://lnkd.in/dQyifKa3
👉Project github.com/Amshaker/MAVOS

💦 ObjectDrop: automagical objects removal 💦

👉#Google unveils ObjectDrop, the new SOTA in photorealistic object removal and insertion. Focus on shadows and reflections, impressive!

👉Review https://t.ly/ZJ6NN
👉Paper https://arxiv.org/pdf/2403.18818.pdf
👉Project https://objectdrop.github.io/

🪼 Universal Mono Metric Depth 🪼

👉ETH unveils UniDepth: metric 3D scenes from solely single images across domains. A novel, universal and flexible MMDE solution. Source code released💙

👉Review https://t.ly/5C8eq
👉Paper arxiv.org/pdf/2403.18913.pdf
👉Code github.com/lpiccinelli-eth/unidepth

🔘 RELI11D: Multimodal Humans 🔘

👉RELI11D is the ultimate and high-quality multimodal human motion dataset involving LiDAR, IMU system, RGB camera, and Event camera. Dataset & Source Code to be released soon💙

👉Review https://t.ly/5EG6X
👉Paper https://lnkd.in/ep6Utcik
👉Project https://lnkd.in/eDhNHYBb

🔥 ECoDepth: SOTA Diffusive Mono-Depth 🔥

👉New SIDE model using a diffusion backbone conditioned on ViT embeddings. It's the new SOTA in SIDE. Source Code released 💙

👉Review https://t.ly/s2pbB
👉Paper https://lnkd.in/eYt5yr_q
👉Code https://lnkd.in/eEcyPQcd

🔥🔥Code is out 🔥🔥

https://github.com/AssafSinger94/dino-tracker

🕷️ Gen-NeRF2NeRF Translation 🕷️

👉GenN2N: unified NeRF-to-NeRF translation for editing tasks such as text-driven NeRF editing, colorization, super-resolution, inpainting, etc.

👉Review https://t.ly/VMWAH
👉Paper arxiv.org/pdf/2404.02788.pdf
👉Project xiangyueliu.github.io/GenN2N/
👉Code github.com/Lxiangyue/GenN2N

👆iSeg: Interactive 3D Segmentation👆

👉 iSeg: interactive segmentation technique for 3D shapes operating entirely in 3D. It accepts both positive/negative clicks directly on the shape's surface, indicating inclusion & exclusion of regions.

👉Review https://t.ly/tyFnD
👉Paper https://lnkd.in/dydAz8zp
👉Project https://lnkd.in/de-h6SRi
👉Code (coming)

👗 Neural Bodies with Clothes 👗

👉Neural-ABC is a novel parametric model based on neural implicit functions that can represent clothed human bodies with disentangled latent spaces for ID, clothing, shape, and pose.

👉Review https://t.ly/Un1wc
👉Project https://lnkd.in/dhDG6FF5
👉Paper https://lnkd.in/dhcfK7jZ
👉Code https://lnkd.in/dQvXWysP

🔌 BodyMAP: human body & pressure 🔌

👉#Nvidia (+CMU) unveils BodyMAP, the new SOTA in predicting body mesh (3D pose & shape) and 3D applied pressure on the human body. Source Code released, Dataset coming 💙

👉Review https://t.ly/8926S
👉Project bodymap3d.github.io/
👉Paper https://lnkd.in/gCxH4ev3
👉Code https://lnkd.in/gaifdy3q

🧞 XComposer2: 4K Vision-Language 🧞

👉InternLMXComposer2-4KHD brings LVLM resolution capabilities up to 4K HD (3840×1600) and beyond. Authors: Shanghai AI Lab, CUHK, SenseTime & Tsinghua. Source Code & Models released 💙

👉Review https://t.ly/GCHsz
👉Paper arxiv.org/pdf/2404.06512.pdf
👉Code github.com/InternLM/InternLM-XComposer

⚛️ Flying w/ Photons: Neural Render ⚛️

👉Novel neural rendering technique that seeks to synthesize videos of light propagating through a scene from novel, moving camera viewpoints. Pico-Seconds time resolution!

👉Review https://t.ly/ZqL3a
👉Paper arxiv.org/pdf/2404.06493.pdf
👉Project anaghmalik.com/FlyingWithPhotons/
👉Code github.com/anaghmalik/FlyingWithPhotons

☄️ Tracking Any 2D Pixels in 3D ☄️

👉 SpatialTracker lifts 2D pixels to 3D using monocular depth, represents the 3D content of each frame efficiently using a triplane representation, and performs iterative updates using a transformer to estimate 3D trajectories.

👉Review https://t.ly/B28Cj
👉Paper https://lnkd.in/d8ers_nm
👉Project https://lnkd.in/deHjtZuE
👉Code https://lnkd.in/dMe3TvFT

🪐YOLO-CIANNA: Neural Astro🪐

👉 CIANNA is a general-purpose deep learning framework for (but not only for) astronomical data analysis. Source Code released 💙

👉Review https://t.ly/441XS
👉Paper arxiv.org/pdf/2402.05925.pdf
👉Code github.com/Deyht/CIANNA
👉Wiki github.com/Deyht/CIANNA/wiki

🧤Neuro MusculoSkeletal-MANO🧤

👉SJTU unveils MusculoSkeletal-MANO, novel musculoskeletal system with a learnable parametric hand model. Source Code announced 💙

👉Review https://t.ly/HOQrn
👉Paper arxiv.org/pdf/2404.10227.pdf
👉Project https://ms-mano.robotflow.ai/
👉Code announced (no repo yet)

⚽SoccerNET: Athlete Tracking⚽

👉SoccerNet Challenge is a novel high level computer vision task that is specific to sports analytics. It aims at recognizing the state of a sport game, i.e., identifying and localizing all sports individuals (players, referees, ..) on the field.

👉Review https://t.ly/Mdu9s
👉Paper arxiv.org/pdf/2404.11335.pdf
👉Code github.com/SoccerNet/sn-gamestate

🎲 Articulated Objs from MonoClips 🎲

👉REACTO is the new SOTA to address the challenge of reconstructing general articulated 3D objects from single monocular video

👉Review https://t.ly/REuM8
👉Paper https://lnkd.in/d6PWagij
👉Project https://lnkd.in/dpg3x4tm
👉Repo https://lnkd.in/dRZWj6_N

🪼 All You Need is SAM (+Flow) 🪼

👉Oxford unveils the new SOTA for moving object segmentation via SAM + Optical Flow. Two novel models & Source Code announced 💙

👉Review https://t.ly/ZRYtp
👉Paper https://lnkd.in/d4XqkEGF
👉Project https://lnkd.in/dHpmx3FF
👉Repo coming: https://github.com/Jyxarthur/

🛞 6Img-to-3D driving scenarios 🛞

👉EPFL (+ Continental) unveils 6Img-to-3D, novel transformer-based encoder-renderer method to create 3D onbounded outdoor driving scenarios with only six pics

👉Review https://shorturl.at/dZ018
👉Paper arxiv.org/pdf/2404.12378.pdf
👉Project 6img-to-3d.github.io/
👉Code github.com/continental/6Img-to-3D