💄MagicMakeup Transfer💄

👉Makeup-transfer applies the reference makeup to the source face while preserving the source identity. Authors: Zhejiang University & vivo BlueImage Lab. Repo for non commercial💙

👉Review https://t.ly/JYpCr
👉Paper harxiv.org/pdf/2607.20924
👉Project vivocameraresearch.github.io/magicmakeup
👉Repo github.com/vivoCameraResearch/Magic-Makeup

🔎MicroZoom at Extreme Scale🔎

👉MicroZoom by UWA synthesizes gigapixel-resolution images grounded in consumer-grade microscope close-ups at magnification levels up to 350×. Impressive. Repo under MIT💙

👉Review https://t.ly/hgJD7
👉Paper https://arxiv.org/pdf/2607.24729
👉Project https://microzoom-sr.github.io/
👉Repo github.com/MicroZoom-SR/MicroZoom-SR.github.io

🍿 Dawn of Generative Cinematography 🍿

🟩 The Odyssey by Christopher Nolan was shot entirely on IMAX 70mm. It feels almost romantic: massive cameras, film stock, premium lenses, and an obsessive pursuit of the highest possible quality at the moment of capture.

👉 Meanwhile, AI research is heading in the exact opposite direction.

🟩 A pre-print paper released today, "Camera Anything", demonstrates something that sounded like science fiction just a few years ago: you film a scene once... and then you can virtually reposition the camera anywhere.

👉More: https://t.ly/g-qUh
👉Paper arxiv.org/pdf/2607.24591
👉Project yixuanli98.github.io/cameraanything/
👉Repo github.com/yixuanli98/CameraAnything

🔥Decoder-only Any-to-Any Model🔥

👉MODUS unifies any-to-any multimodal generation with one decoder, two experts, and zero task heads. Impressive work. Repo under Apache💙

👉Review https://t.ly/-2QKT
👉Paper https://lnkd.in/dhfBQGhB
👉Project https://lnkd.in/dPD_ECXk
👉Repo https://lnkd.in/dbDHw24u

🐠Dual-branch ID-Tracking🐠

👉TIDE: tracking dense, homogeneous targets, providing a scalable dual-branch design to accommodate diverse hardware constraints. MIT license💙

👉Review https://t.ly/WEDeY
👉Paper https://arxiv.org/pdf/2607.26412
👉Project https://vranlee.github.io/TIDE/
👉Repo https://github.com/vranlee/TIDE

🔥Unified Points n' Lines🔥

👉ETH (+Microsoft Spatial AI Lab) unveils a novel feature extractor that jointly extracts keypoints, lines, and feature descriptors within a single lightweight net. SOTA in line detection can be achieved by adding only three convolutional layers to existing point extractor. Repo under Apache💙

👉Review https://lnkd.in/p/eW8j5JZj
👉Paper https://arxiv.org/pdf/2608.19894
👉Repo https://github.com/francois141/upal

🐆 Anyone in 4D is out 🐆

👉4DAnyone turns a casual monocular video into multi-view videos, enabling downstream 4DGS reconstruction. Full repo under Apache 2.0💙

👉Review https://lnkd.in/p/ec4dzGvb
👉Paper https://arxiv.org/pdf/2608.20335
👉Project https://4danyone.github.io
👉Repo github.com/ant-research/4DAnyone

🍋‍🟩Remesh-Aware Mesh Deformation🍋‍🟩

👉RADmesh is a novel generative deformation technique enhanced by remeshing. Given a text prompt, it deforms and remeshes a mesh region to form new geometric features. Repo MIT💙

👉Review https://lnkd.in/p/eK6FZv9c
👉Paper https://arxiv.org/pdf/2608.17182
👉Project https://threedle.github.io/radmesh/
👉Repo https://github.com/threedle/radmesh/

🦑Unified Segmentation n' Retrieval🦑

👉FoundYou gets an example of your object and it segments the same physical instance in a new image or retrieve it from a large gallery with ONE super-compact model. Repo/demo available💙

👉Review https://lnkd.in/p/ex2qnKHW
👉Paper arxiv.org/pdf/2608.29917
👉Project https://lnkd.in/eNEUB_nV
👉Repo https://lnkd.in/eRyDY6Ue

🍚Vision Weight Estimation🍚

👉Doppio is a novel video dataset capturing video of falling ground coffee, paired with precise, per-frame ground-truth weight measurements: OCR readings are extracted from the display, smoothed and time-lag compensated, and paired with per-frame weight annotations. Repo to be released under Apache💙

👉Review https://www.linkedin.com/posts/visionarynet_computer-vision-weight-estimation-activity-7501900695457951744-ArBO
👉Paper https://lnkd.in/eHuy87SX
👉Project https://lnkd.in/e9g9zeK3
👉Repo https://lnkd.in/emUePTiq

🪣Weather-Conditioned Depth Anything🪣

👉Weather-Conditioned Depth Anything from Texas A&M is the new SOTA in weather-robust depth estimation. A curated mix of real and synthetic degradation datasets to extract content-independent, degradation-aware weather embeddings. Repo under Apache💙

👉Review https://lnkd.in/p/eW-dsepD
👉Paper https://lnkd.in/er_MvVft
👉Project https://lnkd.in/ehXPs3C7
👉Repo https://lnkd.in/edk7Ts_r

+++ Mistral raises 3B € +++

👉Discussion: https://lnkd.in/p/eVpF--VW

👻Emerging Objs from Motion👻

👉Motion boundaries provide a strong signal for object-level grouping and can be used to derive pseudo-instance supervision. Suitable for: mono-depth, 3D object detection, 3D occupancy, and end-to-end planning. Repo under Apache 2.0💙

👉Review https://lnkd.in/p/eezZrSJE
👉Paper https://arxiv.org/pdf/2609.04348
👉Project https://tj12342.github.io/object-concepts-from-motion/
👉Repo https://github.com/TJ12342/object-concepts-from-motion/tree/main

🔥#AIwithPapers: we are 17,000+🔥

👉 Even though 100+ bots are trying to join the discussion chats every day, there are 17,000 of us! Almost all of us are still humans 🧟

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

🏀McByte++ tracking-by-detection🏀

👉McByte++ is the newer extension of McByte that advances training-free sports MOT toward long-term ID tracking, while simultaneously improving efficiency and runtime performance. Repo under Apache 2.0💙

👉Review https://lnkd.in/p/e4-diVJS
👉Paper https://lnkd.in/e_Vxky-b
👉Repo https://lnkd.in/e8SeCYmk

🔥🔥 Marigold V2 is out 🔥🔥

👉Marigold V2 is out: depth, (impressive) see-through depth, surface normals, albedo, and other dense modalities. SOTA results. Repo under Apache 2.0💙

#AI #deeplearning #AIwithPapers

👉Review https://lnkd.in/p/eKM44yDQ
👉Paper https://arxiv.org/pdf/2609.08084
👉Repo https://github.com/huawei-bayerlab/marigold-v2
👉Project https://huggingface.co/spaces/huawei-bayerlab/marigold-v2-web

🦺Efficient/Scalable Video Pretraining🦺

👉LeVJEPA1 (Yann Lecun) is the first video encoder trained under LeJEPA’s collapse-free objective, and evaluate it under frozen probing against video and image pretraining baselines retrained on identical data, in both epoch-matched and FLOP-matched regimes. Repo under MIT💙

👉Review https://lnkd.in/p/eJQAm3AN
👉Paper https://lnkd.in/eCzzTiNH
👉Project https://levjepa.github.io/
👉Repo https://lnkd.in/etiF5CDj

🔥 RelateAnything is gold! 🔥

👉RelateAnything is a 53M-parameter relation model that takes an image and a set of regions from any source and returns scored relations over a predicate vocabulary supplied at inference as a list of strings. Impressive results. Repo under Apache 2.0💙

👉Review https://lnkd.in/p/etAcdFM3
👉Paper https://arxiv.org/pdf/2609.12552
👉Repo https://github.com/Maelic/RelateAnything
👉Project https://maelic.github.io/RelateAnythingProject/