AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning, #MachineLearning, LLMs and #ComputerVision

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/

#artificialintelligence #machinelearning #ml #AI
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🌈 Multi-View 3D Tracking 🌈

πŸ‘‰MVTracker is the first data-driven multi-view 3D point tracker for tracking arbitrary 3D points across multiple cameras. Repo availableπŸ’™

πŸ‘‰Review https://t.ly/rISMR
πŸ‘‰Paper arxiv.org/pdf/2508.21060
πŸ‘‰Project https://lnkd.in/drHtAmRC
πŸ‘‰Repo https://lnkd.in/d4k8mg3B
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❀️‍πŸ”₯PHD: Personalized 3D Humans❀️‍πŸ”₯

πŸ‘‰ETH & #Meta unveil PHD, a novel approach for personalized 3D human mesh recovery (HMR) and body fitting that leverages user-specific shape information. Code & models to be releasedπŸ’™

πŸ‘‰Review https://t.ly/IeRhH
πŸ‘‰Paper https://arxiv.org/pdf/2508.21257
πŸ‘‰Project https://phd-pose.github.io/
πŸ‘‰Repo TBA
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πŸͺ΄ Pixie: Physics from Pixels πŸͺ΄

πŸ‘‰UPenn + MIT unveil Pixie: training a neural-net that maps pretrained visual features (i.e., CLIP) to dense material fields of physical properties in a single forward pass, enabling real‑time physics simulations. Repo & Dataset under MIT licenseπŸ’™

πŸ‘‰Review https://t.ly/1W0n5
πŸ‘‰Paper https://lnkd.in/dsHAHDqM
πŸ‘‰Project https://lnkd.in/dwrHRbRc
πŸ‘‰Repo https://lnkd.in/dy7bvjsK
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πŸ«›TMR: Few-Shot Template-matchingπŸ«›

πŸ‘‰POSTECH unveils TMR, a novel and simple template-matching detector for few-shot pattern detection, achieving strong (and SOTA) results on diverse datasets. A new dataset (RPINE) released, repo soonπŸ’™

πŸ‘‰Review https://t.ly/WWAcL
πŸ‘‰Paper https://lnkd.in/dJbSu5vk
πŸ‘‰Project https://lnkd.in/dwcDnHHQ
πŸ‘‰Repo https://lnkd.in/dp7aw8Cs
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🧬 OpenVision 2 is out! 🧬

πŸ‘‰UCSC releases OpenVision2: a novel family of generative pretrained visual encoders that removes the text encoder and contrastive loss, training with caption-only supervision. Fully open, Apache 2.0πŸ’™

πŸ‘‰Review https://t.ly/Oma3w
πŸ‘‰Paper https://arxiv.org/pdf/2509.01644
πŸ‘‰Project https://ucsc-vlaa.github.io/OpenVision2/
πŸ‘‰Repo https://github.com/UCSC-VLAA/OpenVision
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πŸ‰ #DoubleDragon with #AI πŸ‰

πŸ‘‰How Double Dragon would look like in real life? Each character has been transformed with #AI to capture their style, fighting spirit, and charisma, as if they had stepped right out of the game’s streets into the real world. AUDIO ON. Damn romanticπŸ’™

#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse #LLM

πŸ‘‰Post https://t.ly/0IpER
πŸ‘‰Channel https://www.youtube.com/@iaiaoh84
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🍐 Promptable Human Mesh 🍐

πŸ‘‰PromptHMR is a promptable human pose/shape (HPS) estimation method that processes images with spatial or semantic prompts. It takes β€œside information” readily available from vision-language models or user input to improve the accuracy and robustness of 3D HPS. Code releasedπŸ’™

πŸ‘‰Review https://t.ly/zJ7S-
πŸ‘‰Paper arxiv.org/pdf/2504.06397
πŸ‘‰Project yufu-wang.github.io/phmr-page/
πŸ‘‰Repo github.com/yufu-wang/PromptHMR
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πŸ”₯WebEyeTrack: real-time/web eyeπŸ”₯

πŸ‘‰WebEyeTrack is a novel framework that integrates lightweight SOTA gaze estimation models directly in the browser. Bringing deep‑learning gaze estimation to the web browser and explicitly accounts for head pose. Source Code released under MIT licenseπŸ’™

πŸ‘‰Review https://t.ly/Xon9h
πŸ‘‰Paper https://arxiv.org/pdf/2508.19544
πŸ‘‰Project redforestai.github.io/WebEyeTrack/
πŸ‘‰Repo github.com/RedForestAi/WebEyeTrack
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βœ‚οΈ AI Open-Source Annotation βœ‚οΈ

πŸ‘‰VisioFirm by TOELT is a fully open-source, AI-powered image annotation tool designed to accelerate labeling for Computer Vision tasks like object detection, oriented BBs, and segmentation. Source code released under Apache 2.0πŸ’™

πŸ‘‰Review https://t.ly/MoMvv
πŸ‘‰Paper https://lnkd.in/dxTncSgv
πŸ‘‰Repo https://lnkd.in/dCWMXp3x
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What about posting stuff about AI on IG? Thoughts?
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πŸ–ŒοΈReal-Time Drag-Based EditingπŸ–ŒοΈ

πŸ‘‰The Visual AI Lab unveils Inpaint4Drag, a novel framework that decomposes drag-based editing into pixel-space bidirectional warping/inpainting. Inspired by elastic object deformation. Demo and Code released (unknown license)πŸ’™

πŸ‘‰Review https://t.ly/H5nlR
πŸ‘‰Paper https://arxiv.org/pdf/2509.04582
πŸ‘‰Project https://visual-ai.github.io/inpaint4drag/
πŸ‘‰Repo https://github.com/Visual-AI/Inpaint4Drag
πŸ‘‰Demo https://colab.research.google.com/drive/1fzoyNzcJNZjM1_08FE9V2V20EQxGf4PH
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🩸Foundation Red Blood Cells🩸

πŸ‘‰RedDino from University of Cagliari is a self-supervised foundation model designed for red blood cell (RBC) morphology analysis. Trained on 1.25M RBC images, it's the new SOTA in shape classification. Code & Models released under Apache2.0πŸ’™

πŸ‘‰Review https://t.ly/uWAch
πŸ‘‰Paper arxiv.org/pdf/2508.08180
πŸ‘‰Code github.com/Snarci/RedDino
πŸ‘‰Models huggingface.co/collections/Snarcy/reddino-689a13e29241d2e5690202fc
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πŸ‘» From Skin to Skeleton πŸ‘»

πŸ‘‰This paper try unifying the SMPL body model with BSM, a new Biomechanical Skeleton Model. The SKEL model is animatable like SMPL but with fewer, and biomechanically-realistic, degrees of freedom. Model, code, and data available for researchπŸ’™

πŸ‘‰Review https://t.ly/JsI8M
πŸ‘‰Paper arxiv.org/pdf/2509.06607
πŸ‘‰Project https://skel.is.tue.mpg.de/
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🌱 FoMo4Wheat Foundational Model 🌱

πŸ‘‰PheniX Lab et al. unveil a novel family of foundational models tailored for wheat image tasks, suitable for classification, detection, counting and segmentation. Demo, Dataset, Model & Code under MITπŸ’™

πŸ‘‰Review https://t.ly/UzM-Z
πŸ‘‰Paper arxiv.org/pdf/2509.06907
πŸ‘‰Project fomo4wheat.phenix-lab.com/
πŸ‘‰Repo github.com/PheniX-Lab/FoMo4Wheat?
πŸ‘‰Demo fomo4wheat.phenix-lab.com/demos
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πŸ™Human-Centric Video GenerationπŸ™

πŸ‘‰Tsinghua & #ByteDance unveil HuMo: a unified, human-centric video generation framework designed to produce HQ fine-grained, and controllable human videos from multimodal inputs: text prompt following, consistent subject preservation, synchronized audio-driven motion. Repo released under Apache2.0πŸ’™

πŸ‘‰Review https://t.ly/3S8Yb
πŸ‘‰Paper https://arxiv.org/pdf/2509.08519
πŸ‘‰Project https://phantom-video.github.io/HuMo/
πŸ‘‰Repo https://github.com/Phantom-video/HuMo
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πŸ”₯ 21,000+ Hours Dataset πŸ”₯

πŸ‘‰SpatialVID is a novel large-scale video dataset with explicit spatial annotations including camera poses, depth maps, structured captions and serialized motion instructions. The dataset consists of 7,089 hours of real-world dynamic scenes. Repo & Dataset Apache-2.0 πŸ’™

πŸ‘‰Review https://t.ly/Y9o5k
πŸ‘‰Paper arxiv.org/pdf/2509.09676
πŸ‘‰Project nju-3dv.github.io/projects/SpatialVID/
πŸ‘‰Repo github.com/NJU-3DV/spatialVID
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🦠 Segment & Track Any Cell 🦠

πŸ‘‰RWTH unveils a novel zero-shot cell tracking framework by integrating Segment Anything 2 (SAM2) into the tracking pipeline. Source Code releasedπŸ’™

πŸ‘‰Review https://t.ly/n_srg
πŸ‘‰Paper https://arxiv.org/pdf/2509.09943
πŸ‘‰Repo https://github.com/zhuchen96/sam4celltracking
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πŸ”₯ How We Use ChatGPT πŸ”₯

πŸ‘‰By July 2025, ChatGPT has 700M+ users sending more than 2.5B+ messages per day. About 29,000 messages per second. This paper documents eight important facts about ChatGPT usage in the last three years. 63 pages of impressive statistics. To read.πŸ’™

πŸ‘‰Review https://t.ly/QYHSi
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πŸ›‘οΈ3D Prompted Vision-LLMπŸ›‘οΈ

πŸ‘‰#Nvidia unveils SR-3D, a novel aware vision-language model that connects single-view 2D images and multi-view 3D data through a shared visual token space. Flexible region prompting, allowing users to annotate regions with bounding boxes, segmentation masks on any frame, or directly in 3D, without the need for exhaustive multi-frame labeling. Code & Dataset announcedπŸ’™

πŸ‘‰Review https://t.ly/5Y2c5
πŸ‘‰Paper https://arxiv.org/pdf/2509.13317
πŸ‘‰Project https://www.anjiecheng.me/sr3d
πŸ‘‰Repo TBA
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