AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/ #AI #chatGPT
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام AI with Papers - Artificial Intelligence & Deep Learning
تُعد قناة AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 17 261 مشتركاً، محتلاً المرتبة 7 491 في فئة التكنولوجيات والتطبيقات والمرتبة 2 128 في منطقة ماليزيا.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 17 261 مشتركاً.
بحسب آخر البيانات بتاريخ 24 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 258، وفي آخر 24 ساعة بمقدار -2، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 16.76%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 7.11% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 893 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 227 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 16.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل framework, object, dataset, tba, depth.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision
Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/
#AI #chatGPT”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 25 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
17 261
المشتركون
-224 ساعات
+27 أيام
+25830 أيام
أرشيف المشاركات
🍧10,000× faster SAM-3D🍧
👉Fast SAM 3D Body achieves up to 10.9× speedup, over 10,000× faster MHR-to-SMPL conversion -> real-time humanoid control from RGB. Repo available💙
👉Review https://t.ly/uHx84
👉Paper https://arxiv.org/pdf/2603.15603
👉Project yangtiming.github.io/Fast-SAM-3D-Body-Page/
👉Repo https://github.com/yangtiming/Fast-SAM-3D-Body
🤖Physically-Plausible Human🤖
👉PhysMoDPO is a novel direct preference optimization framework for humanoid motion generation. Repo under MIT💙
👉Review https://t.ly/clf8w
👉Paper https://arxiv.org/pdf/2603.13228
👉Project https://mael-zys.github.io/PhysMoDPO/
👉Repo https://github.com/Mael-zys/PhysMoDPO
🌈 New SOTA Video Depth 🌈
👉DVD is the new Video Depth Estimation SOTA with full training suite available under Apache2.0💙
👉Review https://t.ly/gpCkG
👉Paper https://arxiv.org/pdf/2603.12250
👉Project https://dvd-project.github.io/
👉Repo github.com/EnVision-Research/DVD
☄️OmniStream: Perceive-Reconstruct-Act ☄️
👉Novel unified streaming visual backbone that effectively perceives, reconstructs, and acts from diverse visual inputs. Repo/Models announced💙
👉Review https://t.ly/_zZMO
👉Paper arxiv.org/pdf/2603.12265
👉Project go2heart.github.io/omnistream/
👉Repo github.com/Go2Heart/OmniStream
🍓Surface Light Tokenizer🍓
👉Apple unveils LITO a novel latent flow matching model enables HQ image-to-3D. Latent representation that encodes a surface light field into a compact set of latent vectors. Impressive results but no code🥲
👉Review https://t.ly/xcWNe
👉Paper https://lnkd.in/dYHwY4YX
👉Project https://lnkd.in/dtJT8bXy
🔥Holistic 3D Spatial Intelligence🔥
👉Holi-Spatial is the first fully automated pipeline capable of converting raw video streams into holistic 3D spatial annotations without human intervention. Code/Data announced💙
👉Review https://t.ly/PDpr9
👉Paper https://lnkd.in/dTbMuZCm
👉Project https://lnkd.in/d66CYB4q
👉Repo https://lnkd.in/dAGzShXj
📊Real-Time Scene Graph📊
👉REACT++ by Umea University is the new state-of-the-art model for real-time SGG: 20% faster with a gain of 10% in relation prediction accuracy on average. Code under MIT💙
👉Review https://t.ly/c12VX
👉Paper https://arxiv.org/pdf/2603.06386
👉Repo https://github.com/Maelic/SGG-Benchmark
🎪SOTA Arbitrary Tracking🎪
👉TAPFormer is the novel SOTA transformer-based framework that performs asynchronous temporal-consistent fusion of frames and events for robust and high-freq point tracking. Repo & Dataset under MIT💙
👉Review https://t.ly/-q4wm
👉Paper https://arxiv.org/pdf/2603.04989
👉Project http://tapformer.github.io/
👉Repo https://github.com/ljx1002/TAPFormer
🍧Monocular 3D Clothed Human🍧
👉MultiGO++ is a novel framework for monocular 3D clothed human reconstruction via geometry-texture collaboration. New SOTA but no code announced🥲
👉Review https://t.ly/YKY44
👉Paper arxiv.org/pdf/2603.04993
👉Project 3dagentworld.github.io/multigo++
Could be useful for you seeing a few (verified) job posting about AI in this channel?
🍙Any Resolution, Any Geometry🍙
👉Ultra Resolution Geometry Transformer (URGT) for arbitrary resolutions (e.g. 4K, 6K, 8K) depth–normal estimation. New SOTA. Repo under MIT💙
👉Review https://t.ly/HXg1n
👉Paper arxiv.org/pdf/2603.03026
👉Project dreamaker-mrc.github.io/Any-Resolution-Any-Geometry/
👉Repo github.com/Dreamaker-MrC/Any-Resolution-Any-Geometry
🐪DuoMo: Dual Motion Diffusion🐪
👉DuoMo by #Meta is a novel generative method that recovers human motion in world-space coordinates from unconstrained videos with noisy or incomplete observations. Code announced💙
👉Review https://t.ly/dnA3K
👉Paper arxiv.org/pdf/2603.03265
👉Project yufu-wang.github.io/duomo/
👉Repo TBA
🪿All Point Clouds-One Encoder🪿
👉Utonia is a step toward one-from-all and one-for-all point cloud encoder. It pretrains a single encoder on diverse point cloud data and reuses it as a reliable backbone for downstream tasks. Code under Apache 2.0💙
👉Review https://t.ly/yqSyZ
👉Paper https://arxiv.org/pdf/2603.03283
👉Project https://pointcept.github.io/Utonia/
👉Repo https://github.com/Pointcept/Utonia
🍓Fully Offline Mobile-VTON🍓
👉A novel, hq, privacy-preserving framework that enables fully offline virtual try-on on commodity mobile devices using only a single user image and a garment image. Repo announced, to be released💙
👉Review https://t.ly/dsrIn
👉Paper arxiv.org/pdf/2603.00947
👉Project zhenchenwan.github.io/Mobile-VTON/
👉Repo https://github.com/tmllab/2026_CVPR_Mobile-VTON
🦜Geometry-Aware 4D Head🦜
👉 GeoDiff4D is a novel framework that reconstructs animatable 4D head avatars from a single portrait image through geometry-aware diffusion. Code announced💙
👉Review https://t.ly/J9L-t
👉Paper https://lnkd.in/ddpv-78g
👉Project https://lnkd.in/d-vhukyj
👉Repo https://lnkd.in/dzd6mnFv
🧱Solaris: generative #Minecraft🧱
👉NYU unveils Solaris, multiplayer video world model in Minecraft, which generates consistent first-person observations for two players simultaneously. Impressive work. Repo & Dataset💙
👉Review https://t.ly/VrcrT
👉Paper https://arxiv.org/pdf/2602.22208
👉Project https://solaris-wm.github.io/
👉Repo https://github.com/solaris-wm/
🫸 World-Grounded Hand-Object🫸
👉Given SLAMed egocentric videos, unlike existing methods that predict either hands or object poses separately, WHOLE jointly reconstructs coherent hand and object motion in the world space by guiding a generative motion prior. Code announced💙
👉Review https://t.ly/c5w8h
👉Paper https://arxiv.org/pdf/2602.22209
👉Project https://judyye.github.io/whole-www/
👉Repo TBA
🔥New SOTA Planar Tracking🔥
👉WOFTSAM by the Visual Recognition Group (CTU) is a novel planar tracker that combine robust long-term segmentation by SAM2 with 8 degrees-of-freedom homography pose estimation. Repo under BY-NC-SA 4.0💙
👉Review https://t.ly/VUOe5
👉Paper https://lnkd.in/dZfc_DhQ
👉Repo https://lnkd.in/dAcneJGn
🚤Video Neural Compression🚤
👉TeCoNeRV by UMD is a framework for adapting INR hypernetworks to compress videos efficiently at higher resolutions. Impressive results: +5.35dB PSNR @720p on UVG, -36% bitrates & 1.5-3× faster encoding. Code announced💙
👉Review https://t.ly/0AtCK
👉Paper arxiv.org/pdf/2602.16711
👉Project namithap10.github.io/teconerv/
👉Repo github.com/namithap10/TeCoNeRV/
🐙Dex4D: Task-Agnostic Track🐙
👉Dex4D by CMU is a novel approach for unseen objects and poses, scene layouts, backgrounds, & task trajectories. Code under Apache 2.0💙
👉Review https://t.ly/ZGx9T
👉Paper arxiv.org/pdf/2602.15828
👉Project dex4d.github.io/
👉Sim github.com/Dex4D/Dex4D-Simulation
👉Vision github.com/Dex4D/Dex4D-Vision
👉HW https://github.com/Dex4D/Dex4D-Hardware
