AI with Papers - Artificial Intelligence & Deep Learning
رفتن به کانال در Telegram
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
نمایش بیشتر📈 تحلیل کانال تلگرام AI with Papers - Artificial Intelligence & Deep Learning
کانال AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 17 260 مشترک است و جایگاه 7 497 را در دسته فناوری و برنامه‌ها و رتبه 2 130 را در منطقه ماليزيا دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 17 260 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 22 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 253 و در ۲۴ ساعت گذشته برابر -1 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 18.98% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 7.07% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 3 275 بازدید دریافت می‌کند. در اولین روز معمولاً 1 220 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 23 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند framework, object, dataset, tba, depth تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
“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”
به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 23 اکتبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.
17 260
مشترکین
-124 ساعت
+67 روز
+25330 روز
آرشیو پست ها
🍧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
