AI with Papers - Artificial Intelligence & Deep Learning
Open in 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
Show more📈 Analytical overview of Telegram channel AI with Papers - Artificial Intelligence & Deep Learning
Channel AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) in the English language segment is an active participant. Currently, the community unites 17 259 subscribers, ranking 7 509 in the Technologies & Applications category and 2 130 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 259 subscribers.
According to the latest data from 25 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 261 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 16.91%. Within the first 24 hours after publication, content typically collects 7.04% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 918 views. Within the first day, a publication typically gains 1 216 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 16.
- Thematic interests: Content is focused on key topics such as framework, object, dataset, tba, depth.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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”
Thanks to the high frequency of updates (latest data received on 26 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
17 259
Subscribers
+224 hours
+17 days
+26130 days
Loading data
Similar Channels
Tags Cloud
Incoming and Outgoing Mentions
---
---
---
---
---
---
Attracting Subscribers
October '26Oct '26
October '26
+266
in 0 channels
September '26
+109
in 0 channels
Get PRO
August '26
+71
in 2 channels
Get PRO
July '26
+56
in 0 channels
Get PRO
June '26
+52
in 0 channels
Get PRO
May '26
+90
in 0 channels
Get PRO
April '26
+207
in 0 channels
Get PRO
March '26
+718
in 0 channels
Get PRO
February '26
+140
in 1 channels
Get PRO
January '26
+2 306
in 0 channels
Get PRO
December '25
+157
in 1 channels
Get PRO
November '25
+201
in 0 channels
Get PRO
October '25
+243
in 1 channels
Get PRO
September '25
+147
in 3 channels
Get PRO
August '25
+140
in 0 channels
Get PRO
July '25
+162
in 0 channels
Get PRO
June '25
+107
in 0 channels
Get PRO
May '25
+152
in 1 channels
Get PRO
April '25
+331
in 0 channels
Get PRO
March '25
+459
in 1 channels
Get PRO
February '25
+600
in 0 channels
Get PRO
January '25
+643
in 1 channels
Get PRO
December '24
+542
in 1 channels
Get PRO
November '24
+439
in 2 channels
Get PRO
October '24
+350
in 3 channels
Get PRO
September '24
+215
in 0 channels
Get PRO
August '24
+295
in 0 channels
Get PRO
July '24
+396
in 1 channels
Get PRO
June '24
+439
in 0 channels
Get PRO
May '24
+533
in 0 channels
Get PRO
April '24
+483
in 0 channels
Get PRO
March '24
+501
in 1 channels
Get PRO
February '24
+493
in 0 channels
Get PRO
January '24
+522
in 1 channels
Get PRO
December '23
+400
in 0 channels
Get PRO
November '23
+330
in 0 channels
Get PRO
October '23
+298
in 0 channels
Get PRO
September '23
+482
in 0 channels
Get PRO
August '23
+136
in 0 channels
Get PRO
July '23
+173
in 0 channels
Get PRO
June '23
+112
in 0 channels
Get PRO
May '23
+164
in 0 channels
Get PRO
April '23
+234
in 0 channels
Get PRO
March '23
+216
in 0 channels
Get PRO
February '23
+400
in 0 channels
Get PRO
January '23
+435
in 0 channels
Get PRO
December '22
+372
in 0 channels
Get PRO
November '22
+5 238
in 0 channels
| Date | Subscriber Growth | Mentions | Channels | |
| 26 October | 0 | |||
| 25 October | +2 | |||
| 24 October | +1 | |||
| 23 October | +4 | |||
| 22 October | +3 | |||
| 21 October | +7 | |||
| 20 October | +4 | |||
| 19 October | +2 | |||
| 18 October | +4 | |||
| 17 October | +4 | |||
| 16 October | +7 | |||
| 15 October | +2 | |||
| 14 October | +4 | |||
| 13 October | +3 | |||
| 12 October | +5 | |||
| 11 October | +4 | |||
| 10 October | +6 | |||
| 09 October | +3 | |||
| 08 October | +2 | |||
| 07 October | +10 | |||
| 06 October | +7 | |||
| 05 October | +16 | |||
| 04 October | +15 | |||
| 03 October | +64 | |||
| 02 October | +77 | |||
| 01 October | +10 |
Channel Posts
🫀 SKIM V2 is out🫀
👉SOMA recovers muscle deformation over time from surface observations captured by multi-view RGB cameras, the first approach of this kind. SKIM v2: a new soft-tissue deformation dataset. Code under MIT, data & weights NC💙
👉Review https://lnkd.in/p/dB4XS7vB
👉Paper https://lnkd.in/d64S_qxj
👉Project https://lnkd.in/dWtavYGn
👉Repo https://lnkd.in/dXNJqY6N
| 2 |  🧊 SOTA Foundation Pose 🧊
👉From one segmented RGB-D observation, directly predict 6D pose, metric size, and camera-frame completed shape. Repo under Apace 2.0💙
👉Review https://lnkd.in/p/dXpXt7Pz
👉Paper https://arxiv.org/pdf/2610.31623
👉Project https://jrryzh.github.io/F2G-Pose/
👉Repo https://github.com/jrryzh/F2G-Pose | 1 398 |
| 3 |  🍵3D from Single Human RGB🍵
👉Real-to-semantic-to-real pipeline that turns one monocular RGB video of a human demonstration into thousands of robot demonstrations. Code TBA💙
👉Review https://lnkd.in/p/dt-B2QMt
👉Paper https://arxiv.org/pdf/2610.28880
👉Project https://real2sem2real.github.io/
👉Repo TBA | 2 113 |
| 4 |  🥭Agentic Manga->Animation🥭
👉Manga2Animate: turning existing manga pages into coherent animation that stays faithful to the original manga. Impressive results, best with sound. Code released.💙
👉Review https://lnkd.in/p/dHpdPAj3
👉Paper https://arxiv.org/pdf/2610.26851
👉Project https://annchenn.github.io/Manga2Animate/
👉Repo https://github.com/annchenn/Manga2Animate | 2 103 |
| 5 |  🔥SOTA Video Matting (long clips)🔥
👉VideoMaMa++ (#NeuroIPS 2026): mask-guided video matting framework that recovers fine-grained details (e.g., hair strands) and scales reliably to long videos. Impressive results, NC code announced💙
👉Review https://lnkd.in/p/dj3_zE3P
👉Paper https://arxiv.org/pdf/2610.26857
👉Project https://cvlab-kaist.github.io/VideoMaMa-PlusPlus/
👉Repo https://github.com/cvlab-kaist/VideoMaMa-PlusPlus | 2 252 |
| 6 |  🏵️Lang-Informed Lossless Encoder🏵️
👉Stanford's LILIE: an invertible image transform trained such that a vision-language model can recover captions from noised features. Code, encoders, and generative models💙
👉Review https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2610.23653
👉Project https://www.suchiragarwal.com/lilie/
👉Repo https://github.com/SuchirAgarwal/lilie | 2 305 |
| 7 |  🔥700 FPS Humans Recovery🔥
👉One model, any sparse signal: OmniPoser recovers full-body 3D human motion from 3D keypoints, sparse IMU rotations, head-mounted 6-DoF poses, or any subset or combination of these signals. All within a SINGLE model. Repo under nc-cc license💙
👉Review https://lnkd.in/p/e-xAquHt
👉Paper https://arxiv.org/pdf/2610.19857
👉Project https://jamesbridgewater51.github.io/omniposer-eccv/
👉Repo https://github.com/JamesBridgewater51/omniposer | 3 085 |
| 8 | Back to post on Twitter 😈
https://x.com/ai_withpapers | 2 664 |
| 9 |  🎊 Pixel-Level 4D Humans 🎊
👉Dense4D from the Imperial College is the first feed-forward model dedicated to dense 4D correspondence across the body, clothing and hair from a set of monocular RGB images, without a body template. Code announced💙
👉Review https://lnkd.in/p/e9GXGggz
👉Paper https://arxiv.org/pdf/2610.22325
👉Project https://alpguler.com/dense4d/
👉Repo TBA | 2 639 |
| 10 |  🧊SOTA category-level 9D Pose🧊
👉SJTU at #NeurIPS 2026 with the new SOTA among RGB-based methods for category-level 9D pose estimation. Code announced💙
👉Review https://lnkd.in/p/e5BvM7Ua
👉Paper https://arxiv.org/pdf/2610.18374
👉Project https://souglad21.github.io/SchemaPose/
👉Repo TBA | 2 754 |
| 11 |  🐇Segment where you look🐇
👉KAUST (+Adobe) revisited the interface through which a vision-language model passes the location of its referent to a segmentation decoder. Code announced💙
👉Review https://lnkd.in/p/er_ifxGR
👉Paper https://lnkd.in/eATXw98R
👉Project https://lnkd.in/eXSWvxPN
👉Repo TBA | 2 567 |
| 12 | Hi everyone 👋
over the past months, engagement on my posts has grown a lot, which tells me the content you value is getting better. At the same time, impressions have dropped, and I think a big part of the reason is the flood of low-quality viral posts filling many of our feeds. PROVE ME WRONG 😂
The best way to push back is to engage with the posts that are actually worth your time. Open the post on LinkedIn, then leave a comment (even a couple of lines with your take), and like, share, or save it if it's useful. Comments are the most valuable, but every action helps. With ~100,000 of us following along, this is how we tell the algorithm, and me, what deserves more space.
Fight fire with fire! | 3 023 |
| 13 |  🥬 Rotational neural keypoint 🥬
👉RaCo is a rotationally equivariant neural keypoint detector that identifies stable and repeatable keypoints. Code under Apache 2.0💙
👉Review https://lnkd.in/p/ezQzUwju
👉Paper https://arxiv.org/pdf/2602.15755
👉Repo https://github.com/cvg/RaCo | 3 251 |
| 14 |  🫏What EGO does not teach🫏
👉A brilliant work about a controlled study of how interaction fidelity in ego-centric world models behaves as a function of scale. Closing this gap will depend on how models are trained, not only on how much data they see. Repo available soon💙
👉Review https://lnkd.in/p/dzKbCCfr
👉Paper https://lnkd.in/dcpReY9E
👉Project https://lnkd.in/dnpfpZnG
👉Repo TBA | 1 179 |
| 15 |  🟨 Rome from ONE pic 🟥
👉Detailed scene meshes from one photograph: the method completes geometry beyond the observed view and supports indoor, outdoor, and large-scale scenes. Repo announced💙
👉Review https://lnkd.in/p/eMdWBEGn
👉Paper https://arxiv.org/pdf/2610.08790
👉Project https://build-rome.github.io/
👉Repo TBA | 4 559 |
| 16 |  🐇Physically Plausible 3D Motion🐇
👉Physically plausible motion recovery: given a monocular video, FlowHMR recovers global 3D human motion that a physics-based controller can successfully track in simulation. Repo💙
👉Review https://lnkd.in/p/d77fzUtR
👉Paper https://arxiv.org/pdf/2610.03691
👉Project https://flowhmr.github.io/
👉Repo https://github.com/flowhmr/flowhmr | 4 364 |
| 17 |  🔥The Computer Vision ultimate collection🔥
👉Stan Birchfield (#Nvidia) just dropped this on arXiv. From classical image processing and 3D geometry to CNNs, Transformers, foundation models, and neural rendering. What makes this book damn good is the combination of clear explanations and working Python. A gift.
👉Review https://lnkd.in/p/ejwm_DVn
👉Book https://lnkd.in/eTrEvmd9
👉Code https://lnkd.in/eakj9VZU | 5 620 |
| 18 |  🔥🔥 70,000+ 🔥🔥
👉 Crazy how a boring science project (no kittens, no rants, no personal dramas) can reach for 70,000+ followers. Speechless.
Love u 💛
👉 https://lnkd.in/p/eD6Xxdxi | 4 338 |
| 19 |  🔥Ego-Exo4D Human Dataset🔥
👉Form the University of Austin, Ego-Exo4D-HM: large-scale dataset of 4D human motion reconstructions for Ego-Exo4D’s captures + reconstruction pipeline. Code, dataset, and docs released💙
👉Review https://lnkd.in/p/eVFt9jPr
👉Paper https://lnkd.in/eWj4cD7T
👉Project https://lnkd.in/euPqVNxV | 3 990 |
| 20 |  🔥TrackEverything is out🔥
👉TrackEverything is the first 3D point tracker capable of tracking all visible points across long horizons (1000+ frames). Repo announced💙
👉Review https://lnkd.in/p/eCPJ6h2B
👉Paper https://arxiv.org/pdf/2609.30222
👉Project https://trackeverything.github.io/
👉Repo https://github.com/ayushjain1144/trackeverything | 4 370 |
