🎇 Multi-target SAM3 🎇

👉SAM3-DMS is a novel training-free decoupled strategy that utilizes fine-grained memory selection on individual objects. Robust identity preservation and tracking stability. Repo under SAM License💙

👉Review https://t.ly/jJOAr
👉Paper https://arxiv.org/pdf/2601.09699
👉Repo https://github.com/FudanCVL/SAM3-DMS

🍿100M Video Action Dataset🍿

👉Action100M by META is a large-scale dataset w/ 1.2M instructional videos (14.6 years of duration), yielding O(100M) temporally localized segments with open-vocabulary action supervision and rich captions. Repo under FAIR NC Research License💙

👉Review https://t.ly/w5KXe
👉Paper arxiv.org/pdf/2601.10592
👉Repo github.com/facebookresearch/Action100M

💜Interactive Humanoid Generation💜

👉FlowAct-R1 by ByteDance is a novel framework that enables lifelike, responsive, and high-fidelity humanoid video generation for seamless real-time interaction. No code but impressive results (see video with audio) 💙

👉Review https://t.ly/aQhol
👉Paper arxiv.org/pdf/2601.10103
👉Project grisoon.github.io/FlowAct-R1/

💢3D Human Gen-Seg💢

👉CoMoVi takes an input image with a text description and generates 3D human motion & video sequence synchronously within a single diffusion denoising loop. Repo & Dataset releasing💙

👉Review https://t.ly/khSkm
👉Paper arxiv.org/pdf/2601.10632
👉Project igl-hkust.github.io/CoMoVi/
👉Repo github.com/IGL-HKUST/CoMoVi
👉Data huggingface.co/datasets/AfterJourney/CoMoVi-Dataset

👹SOTA Part-level Generator👹

👉A novel a text-to-motion model that learns to compose complex motions through hierarchical conditioning on part-, action- & sequence-level text, enabling fine-grained control over body parts & timing. Code, models & Dataset to be released💙

👉Review https://t.ly/leB_R
👉Paper arxiv.org/pdf/2601.10909
👉Project coral79.github.io/frankenmotion/
👉Repo github.com/Coral79/FrankenMotion-Code

💚 #META 3D Casual Captures 💚

👉#META unveils ShapeR, a novel approach for conditional 3D object shape generation from casually captured sequences. Impressive results. Repo under CC BY-NC 4.0💙

👉Review https://t.ly/j08sJ
👉Paper arxiv.org/pdf/2601.11514
👉Project facebookresearch.github.io/ShapeR/
👉Repo github.com/facebookresearch/ShapeR

💊Foundation Medical SAM3 💊

👉Medical SAM3: foundation model for universal prompt-driven medical image segmentation, by fully fine-tuning SAM3 on large-scale, heterogeneous 2D/3D medical imaging datasets with paired segmentation masks-text prompts. Repo & Demo announced💙

👉Review https://t.ly/C6jcy
👉Paper https://arxiv.org/pdf/2601.10880
👉Project chongcongjiang.github.io/MedicalSAM3/#
👉Repo github.com/AIM-Research-Lab/Medical-SAM3

🦧Mask-Guided Matting🦧

👉VideoMaMa is novel a diffusion-based model that converts binary masks into continuous alpha mattes. Repo, Dataset & Demo💙

👉Review https://t.ly/l_0f8
👉Paper arxiv.org/pdf/2601.14255
👉Project cvlab-kaist.github.io/VideoMaMa
👉Repo github.com/cvlab-kaist/VideoMaMa
👉Demo huggingface.co/spaces/SammyLim/VideoMaMa

💜MoRo: Human Motion💜

👉Masked modeling for human motion Recovery under Occlusions. Given a monocular video captured from a static camera, MoRo (by ETHZ & META) robustly reconstructs accurate/physically plausible human motion, even under challenging occlusions. Repo released💙

👉Review https://t.ly/kK_je
👉Paper arxiv.org/pdf/2601.16079
👉Project mikeqzy.github.io/MoRo/
👉Repo github.com/mikeqzy/MoRo

🔥 BBoxMaskPose v2 is fire 🔥

👉BBoxMaskPose v2 by ČVUT offers SOTA performance in detection, segmentation & 2D pose in crowded scenes. It enables 3D human reconstruction even in scenes with complex interactions. Code, Models & data available💙

👉Review https://t.ly/GkkDl
👉Paper arxiv.org/pdf/2601.15200
👉Project https://lnkd.in/dQ_3hxjC
👉Repo https://lnkd.in/dVqwD3jN

🦠Generalized-Scale Counting🦠

👉GeCo2 (Ljubljana) is a novel e2e SOTA few-shot method that explicitly addresses the object scale issues. Repo & Demo 💙

👉Review https://t.ly/2_7I8
👉Paper https://arxiv.org/pdf/2511.08048
👉Repo https://github.com/jerpelhan/GECO2
👉Demo huggingface.co/spaces/jerpelhan/GECO2-demo

🔥🔥Super-Hard Poll folks🔥🔥

👉 This dilemma is driving me crazy. Vote: https://www.linkedin.com/posts/visionarynet_activity-7421974594917588992-YNAG

(and of course comment here)

🌻MLLMs Fine Segmentation🌻

👉SimpleSeg: MLLMs with native pixel-level perception. Repo & Model available💙

👉Review https://t.ly/eVguh
👉Paper arxiv.org/pdf/2601.19228
👉Project simpleseg.github.io/
👉Repo github.com/songtianhui/SimpleSeg

🔥 DeepSeek-OCR 2 is out 🔥

👉DeepSeek-AI announced the new version of its powerful SOTA OCR. A new architectural approach with the potential to achieve genuine 2D reasoning. Codes & weights💙

👉Review https://t.ly/gX4bX
👉Paper https://arxiv.org/pdf/2601.20552
👉Repo github.com/deepseek-ai/DeepSeek-OCR-2

📊 SOTA Style Transfer 📊

👉TeleAI unveils TeleStyle, a lightweight yet effective model for image/video stylization. Built upon Qwen-Image-Edit, TeleStyle leverages the base model’s robust capabilities in content preservation & style customization. Code & Model released💙

👉Review https://t.ly/viVR0
👉Paper arxiv.org/pdf/2601.20175
👉Project tele-ai.github.io/TeleStyle/
👉Repo github.com/Tele-AI/TeleStyle

🍑 Metric Anything is out 🍑

👉Metric Anything (Li Auto inc.) is a simple and scalable pretraining framework that learns metric depth from noisy, diverse 3D sources without manually engineered prompts, camera-specific modeling, or task-specific architectures. Impressive. Code announced 💙

👉Review https://t.ly/54Ccr
👉Paper arxiv.org/pdf/2601.22054
👉Project metric-anything.github.io/metric-anything-io/
👉Repo github.com/metric-anything/metric-anything

The hottest website on Earth right now: https://www.moltbook.com

What do you think about it?

🌈Segment Any Events by Language🌈

👉SEAL (by NUS) is the first Semantic-aware Segment Any Events framework that addresses Open-Vocabulary Event Instance Segmentation. Code announced💙

👉Review https://t.ly/1ZMF0
👉Paper https://arxiv.org/pdf/2601.23159
👉Project https://0nandon.github.io/SEAL/
👉Repo https://github.com/0nandon/SEAL

👉RAM prices skyrocketing

👉Me acting like a rich kid.

Let's talk: https://www.linkedin.com/posts/visionarynet_ai-ram-ddr5-activity-7424127924020072448-NbaO