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

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马来西亚2 244 技术与应用7 718...

📈 Telegram 频道 AI with Papers - Artificial Intelligence & Deep Learning 的分析概览

频道 AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) 英语语言赛道中的是活跃参与者。目前社区聚集了 17 146 名订阅者，在 技术与应用 类别中位列第 7 718，并在 马来西亚 地区排名第 2 244 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 17 146 名订阅者。

根据 22 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -178，过去 24 小时变化为 -15，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 24.30%。内容发布后 24 小时内通常能获得 6.86% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 4 167 次浏览，首日通常累积 1 177 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 26。
主题关注点： 内容集中在 framework, object, dataset, tba, depth 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“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”

凭借高频更新（最新数据采集于 23 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

17 146

订阅者

-1524 小时

-437 天

-17830 天

4 167

帖子浏览量

~ 1 17724 小时

~ 1 35348 小时

24.30%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

17 149

🎪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

17 149

🍧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++

17 149

Could be useful for you seeing a few (verified) job posting about AI in this channel?

Anonymous voting

17 149

🍙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

17 149

🐪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

17 149

🪿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

17 149

🍓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

17 149

🦜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

17 149

🧱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/

17 149

🫸 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

17 149

🔥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

17 149

🚤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/

17 149

🐙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

17 149

📲 Efficient VLMs 📲 👉CoPE-VideoLM is a codec-aware tokenization framework for VLM that replaces dense RGB encoding with lightweight structured representations derived from codec primitives. Token -93% / time-to-first-token -86%! Code announced💙 👉Review https://t.ly/3_GqN 👉Paper https://arxiv.org/pdf/2602.13191 👉Project https://sayands.github.io/cope/ 👉Repo TBA

17 149

🥝Conversational Segmentation🥝 👉CIS grounds abstract, intent-oriented concepts into pixel-accurate masks, reasoning about affordances, physics, and functional properties. Code/Demo released💙 👉Review https://t.ly/SsG57 👉Paper arxiv.org/pdf/2602.13195 👉Project glab-caltech.github.io/converseg/ 👉Repo github.com/AadSah/ConverSeg 👉Demo glab-caltech.github.io/converseg/#interactive-demo

17 149

🪿Teaching AI to illusions🪿 👉Stroke of Surprise by NYCU is a novel generative framework that optimizes vector strokes to satisfy distinct semantic interpretations at different drawing stages. As strokes are progressively added, the sketch reveals a completely different subject. Code released💙 👉Review https://t.ly/98Oim 👉Paper https://lnkd.in/dTA7iuce 👉Project https://lnkd.in/dhTMGw23 👉Repo https://lnkd.in/deQyDGFu

17 149

🫧SurfPhase: 3D Interfacial Dynamics🫧 👉SurfPhase is a novel model for reconstructing 3D interfacial dynamics from sparse camera views. Repo/Dataset announced💙 👉Review https://t.ly/g2P5F 👉Paper https://arxiv.org/pdf/2602.11154 👉Project https://yuegao.me/SurfPhase/ 👉Repo github.com/yuegao/SurfPhase

17 149

🤖Generalized Human Tracking🤖 👉Beijing Institute of Technology & Humanoid Robotics Shangai present a novel learning framework for general humanoid whole-body control. Impressive results in imitation. 👉Review https://t.ly/ucmuB 👉Paper arxiv.org/pdf/2601.23080 👉Project zeonsunlightyu.github.io/RGMT.github.io

17 149

🛠️ IndustryShapes 6D Pose 🛠️ 👉IndustryShapes by NTUA is a new RGB-D dataset of industrial tools and components, designed for both instance-level and novel object 6D pose estimation. Dataset available💙 👉Review https://t.ly/KKcuH 👉Paper https://arxiv.org/pdf/2602.05555 👉Project https://pose-lab.github.io/IndustryShapes/ 👉Dataset https://huggingface.co/datasets/POSE-Lab/IndustryShapes

17 149

🛠️ IndustryShapes 6D Pose 🛠️ 👉IndustryShapes by NTUA is a new RGB-D dataset of industrial tools and components, designed for both instance-level and novel object 6D pose estimation. Dataset available💙 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://arxiv.org/pdf/2602.05555 👉Project https://pose-lab.github.io/IndustryShapes/ 👉Dataset https://huggingface.co/datasets/POSE-Lab/IndustryShapes