🌈FlowWAM: flow->action prediction🌈

👉FlowWAM is a novel dual-stream diffusion framework that adopts optical flow as a unified, video-native action representation. Repo under Apache💙

👉Review https://t.ly/FmutT
👉Paper https://arxiv.org/abs/2607.13017
👉Project https://flow-wam.github.io/
👉Repo github.com/YixiangChen515/FlowWAM

🏯SOTA Music-to-Dance Gen🏯

👉The Tongyi Lab unveils Wan-Dancer, a novel stable minute-scale synthesis at 720p/30fps across five dance genres. Impressive results, new SOTA on long clip by a large margin. Repo under Apache 2.0💙

👉Review https://t.ly/AKY5j
👉Paper https://lnkd.in/d_xA7dwb
👉Project https://lnkd.in/dzfnw2h4
👉Repo https://lnkd.in/d-Zj_cTf

👉Not a render. Not a concept. This is GENE.01 by Generative Bionics, the Italians coolest scaleup strikes back: in just six months, they turned GENE.01 into a fully functional humanoid platform that can walk, sense and interact.

👉Full-body multimodal skin perceives touch, proximity, force and temperature, bringing Physical AI closer to safe and natural collaboration with people.

👉More: https://t.ly/F3I3A

🦜Streaming 4D Transformer🦜

👉IGGT4D is a novel a streaming instance-grounded geometry transformer for online 4D scene understanding. It processes video frames sequentially, reuses historical context through causal spatial-temporal modeling, and incrementally updates a unified representation of camera motion, geometry, and object identity. Repo/Data announced💙

👉Review https://t.ly/LFrKR
👉Paper https://arxiv.org/pdf/2607.19228
👉Project https://iggt4d.github.io/
👉Repo TBA

🫛Spatially-Aware Class-Agnostic Counting🫛

👉UpCount is reference-free, spatially aware, class-agnostic object counting with an MAE-pretrained ViT, DPT-style feature reassembly, FeatUp-style joint bilateral upsampling & proposal verification. Repo MIT💙

👉Review https://t.ly/dWOc3
👉Paper https://arxiv.org/pdf/2607.16826
👉Repo github.com/r28112072-rgb/upcount

💢Unified Video Dense Prediction💢

👉UniD predicts: depth, surface normals, semantic segmentation, boundaries, human parts, albedo, shading, and materials. Code TBR💙

👉Review https://t.ly/oo7et
👉Paper https://arxiv.org/pdf/2607.21592
👉Project https://unid-video.github.io/
👉Repo https://github.com/YihongSun/UniD

💄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