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
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 17 261 subscribers, ranking 7 500 in the Technologies & Applications category and 2 124 in the Malaysia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 261 subscribers.
According to the latest data from 24 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 258 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 16.76%. Within the first 24 hours after publication, content typically collects 7.11% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 893 views. Within the first day, a publication typically gains 1 227 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 25 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.
17 261
Subscribers
-224 hours
+27 days
+25830 days
Posts Archive
🐻StableNormal: Stable/Sharp Normal🐻
👉Alibaba unveils StableNormal, a novel method which tailors the diffusion priors for monocular normal estimation. Hugging Face demo is available💙
👉Review https://t.ly/FPJlG
👉Paper https://arxiv.org/pdf/2406.16864
👉Demo https://huggingface.co/Stable-X
🧬 Event-driven SuperResolution 🧬
👉USTC unveils EvTexture, the first VSR method that utilizes event signals for texture enhancement. It leverages high-frequency details of events to better recover texture regions in VSR. Source Code available💙
👉Review https://t.ly/zlb4c
👉Paper arxiv.org/pdf/2406.13457
👉Code github.com/DachunKai/EvTexture
🤓 Glasses-Removal from Videos🤓
👉Lightricks unveils a novel method able to receive an input video of a person wearing glasses, and consistently removes the glasses, while preserving the ID. It works even when there are reflections, heavy makeup, and eye blinks. Code announced, not yet released💙
👉Review https://t.ly/Hgs2d
👉Paper arxiv.org/pdf/2406.14510
👉Project https://v-lasik.github.io/
👉Code github.com/v-lasik/v-lasik-code
🌱 TokenHMR : new 3D human pose SOTA 🌱
👉TokenHMR is the new SOTA HPS method mixing 2D keypoints and 3D pose accuracy, thus leveraging Internet data without known camera parameters. It's the new SOTA by a large margin.
👉Review https://t.ly/K9_8n
👉Paper arxiv.org/pdf/2404.16752
👉Project tokenhmr.is.tue.mpg.de/
👉Code github.com/saidwivedi/TokenHMR
💦 Self-driving in wet conditions 💦
👉BMW SemanticSpray: novel dataset contains scenes in wet surface conditions captured by camera, LiDAR and radar. Camera: 2D Boxes | LiDAR: 3D Boxes, Semantic Labels | Radar: Semantic Labels.
👉Review https://t.ly/8S93j
👉Paper https://lnkd.in/dnN5MCZC
👉Project https://lnkd.in/dkUaxyEF
👉Data https://lnkd.in/ddhkyXv8
🧤HOT3D Hand/Object Tracking🧤
👉#Meta opens a novel egocentric dataset for 3D hand & object tracking. A new benchmark for vision-based understanding of 3D hand-object interactions. Dataset available 💙
👉Review https://t.ly/cD76F
👉Paper https://lnkd.in/e6_7UNny
👉Data https://lnkd.in/e6P-sQFK
🌵 RobustSAM for Degraded Images 🌵
👉RobustSAM, the evolution of SAM for degraded images; enhancing the SAM’s performance on low-quality images while preserving prompt-ability & zeroshot generalization. Dataset & Source Code released💙
👉Review https://t.ly/mnyyG
👉Paper arxiv.org/pdf/2406.09627
👉Project robustsam.github.io
👉Code github.com/robustsam/RobustSAM
📫 MeshPose: DensePose + HMR 📫
👉MeshPose: novel approach to jointly tackle DensePose and Human Mesh Reconstruction in a while. A natural fit for #AR applications requiring real-time mobile inference.
👉Review https://t.ly/a-5uN
👉Paper https://arxiv.org/pdf/2406.10180
👉Project https://meshpose.github.io/
🎹 PianoMotion10M for gen-hands 🎹
👉PianoMotion10M: 116 hours of piano playing videos from a bird’s-eye view with 10M+ annotated hand poses. A big contributions in hand motion generation. Code & Dataset released💙
👉Review https://t.ly/_pKKz
👉Paper arxiv.org/pdf/2406.09326
👉Code https://lnkd.in/dcBP6nvm
👉Project https://lnkd.in/d_YqZk8x
👉Dataset https://lnkd.in/dUPyfNDA
🍉 MASA: MOT Anything By SAM 🍉
👉MASA: Matching Anything by Segmenting Anything pipeline to learn object-level associations from unlabeled images of any domain. An universal instance appearance model for matching any objects in any domain. Source code in June 💙
👉Review https://t.ly/pKdEV
👉Paper https://lnkd.in/dnjuT7xm
👉Project https://lnkd.in/dYbWzG4E
👉Code https://lnkd.in/dr5BJCXm
👑 Kling AI vs. OpenAI Sora 👑
👉Kling: the ultimate Chinese text-to-video model - rival to #OpenAI’s Sora. No papers or tech info to check, but stunning results from the official site.
👉Review https://t.ly/870DQ
👉Paper ???
👉Project https://kling.kuaishou.com/
👗 SOTA Multi-Garment VTOn Editing 👗
👉#Google (+UWA) unveils M&M VTO, novel mix 'n' match virtual try-on that takes as input multiple garment images, text description for garment layout and an image of a person. It's the new SOTA both qualitatively and quantitatively. Impressive results!
👉Review https://t.ly/66mLN
👉Paper arxiv.org/pdf/2406.04542
👉Project https://mmvto.github.io
🧊 Universal 6D Pose/Tracking 🧊
👉Omni6DPose is a novel dataset for 6D Object Pose with 1.5M+ annotations. Extra: GenPose++, the novel SOTA in category-level 6D estimation/tracking thanks to two pivotal improvements.
👉Review https://t.ly/Ywgl1
👉Paper arxiv.org/pdf/2406.04316
👉Project https://lnkd.in/dHBvenhX
👉Lib https://lnkd.in/d8Yc-KFh
🚙 UA-Track: Uncertainty-Aware MOT🚙
👉UA-Track: novel Uncertainty-Aware 3D MOT framework which tackles the uncertainty problem from multiple aspects. Code announced, not released yet.
👉Review https://t.ly/RmVSV
👉Paper https://arxiv.org/pdf/2406.02147
👉Project https://liautoad.github.io/ua-track-website
📞FacET: VideoCall Change Your Expression📞
👉Columbia University unveils FacET: discovering behavioral differences between conversing face-to-face (F2F) and on video-calls (VCs).
👉Review https://t.ly/qsQmt
👉Paper arxiv.org/pdf/2406.00955
👉Project facet.cs.columbia.edu/
👉Repo (empty) github.com/stellargo/facet
👹👹 AI and the Everything in the Whole Wide World Benchmark 👹👹
👉Last week Yann LeCun said something like "LLMs will not reach human intelligence". It's clear the on-going #deeplearning is not ready for "general AI", a "radical alternative" is necessary to create the “superintelligence”.
👉Review https://t.ly/isdxM
👉Paper https://lnkd.in/dFraieZS
👉News https://lnkd.in/da-7PnVT
👹👹👹👹
Last week Yann LeCun said something like "LLMs will not reach human intelligence". It's clear the on-going #deeplearning is not ready for "general AI", a "radical alternative" is necessary to create the “superintelligence”.
🐳 MultiPly: in-the-wild Multi-People 🐳
👉MultiPly: novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos. It's the new SOTA over the publicly available datasets and in-the-wild videos. Source Code announced, coming💙
👉Review https://t.ly/_xjk_
👉Paper arxiv.org/pdf/2406.01595
👉Project eth-ait.github.io/MultiPly
👉Repo github.com/eth-ait/MultiPly
🐳MultiPly: in-the-wild Multi-People from Mono🐳
👉MultiPly: novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos. It's the new SOTA over the publicly available datasets and in-the-wild videos. Source Code announced, coming💙
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2406.01595
👉Project https://eth-ait.github.io/MultiPly/
👉Repo https://github.com/eth-ait/MultiPly
🎭New 2D Landmarks SOTA🎭
👉Flawless AI unveils FaceLift, a novel semi-supervised approach that learns 3D landmarks by directly lifting (visible) hand-labeled 2D landmarks and ensures better definition alignment, with no need for 3D landmark datasets. No code announced🥹
👉Review https://t.ly/lew9a
👉Paper arxiv.org/pdf/2405.19646
👉Project davidcferman.github.io/FaceLift
