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

Malaysia2 241 Technologies & Applications7 723...

📈 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 17 147 subscribers, ranking 7 723 in the Technologies & Applications category and 2 241 in the Malaysia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 17 147 subscribers.

According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -190 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 25.09%. Within the first 24 hours after publication, content typically collects 6.86% reactions from the total number of subscribers.
Post reach: On average, each post receives 4 302 views. Within the first day, a publication typically gains 1 177 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 26.
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 24 June, 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.

17 147

Subscribers

-224 hours

-367 days

-19030 days

4 302

Post views

~ 1 17724 hours

~ 1 35348 hours

25.09%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

17 144

Here the preview, tomorrow the full clip from official source :)

17 144

Hinton our guest in Pavia (remotely) 💚😈

17 144

🔥BoxerNet: SOTA 2D->3D BBs🔥 👉Boxer by #META: transformer-based network to lift 2D BB proposals into 3D, followed by multi-view fusion and geometric filtering to produce globally consistent de-duplicated 3DBBs in metric world space. Repo under A-NC 4.0 International💙 👉Review https://t.ly/mlmV1 👉Paper https://arxiv.org/pdf/2604.05212 👉Project facebookresearch.github.io/boxer/ 👉Repo github.com/facebookresearch/boxer

17 144

🔥Vanast: VTON w/ Human Animation🔥 👉SNU unveils a novel unified framework that generates garment-transferred human animation videos directly from a single human/garment images, and pose guidance clip. Repo announced💙 👉Review https://t.ly/c0t79 👉Paper arxiv.org/pdf/2604.04934 👉Project hyunsoocha.github.io/vanast/ 👉Repo github.com/snuvclab/vanast

17 144

🍎Video Object Deletion🍎 👉Void by Netflix is a novel video object removal framework designed to perform physically-plausible inpainting in very complex scenarios. Repo under Apache 2.0💙 👉Review https://t.ly/cMVny 👉Paper https://arxiv.org/pdf/2604.02296 👉Project https://void-model.github.io/ 👉Repo https://github.com/Netflix/void-model

17 144

If you have to invest TODAY 1B$ on a frontier tech for the next decade, would you invest in space, agentic, quantum or frugal GPUs? Vote here: https://t.ly/hSx6i

17 144

🪬Camera Raw Image Generation🪬 👉RawGen by #Samsung is a generative approach that learns the complex distribution of raw sensor data directly, enabling high-fidelity generation from either text descriptions or standard sRGB images across arbitrary camera sensors. Linear raw image once, then apply any ISP operation. Repo announced💙 👉Review https://t.ly/_QVKP 👉Paper https://arxiv.org/pdf/2604.00093 👉Project https://dy112.github.io/rawgen-page/ 👉Repo TBA

17 144

🌵SOTA Training-Free In-Context Segmentation🌵 👉INSID3 is the new SOTA, training-free approach that segments concepts at varying granularities only from frozen DINOv3 features, given an in-context example. Repo under Apache 2.0💙 👉Review https://t.ly/NVWHN 👉Paper https://arxiv.org/pdf/2603.28480 👉Project https://visinf.github.io/INSID3/ 👉Repo https://github.com/visinf/INSID3

17 144

👌HandX: Scaling Hands Motion👌 👉 HandX is a unified foundation spanning data, annotation, and evaluation: novel large-scale dataset of bimanual & dexterous motions with fine-grained textual. Around 6M frames. Repo available💙 👉Review https://t.ly/1nGxw 👉Paper https://arxiv.org/pdf/2603.28766 👉Project https://handx-project.github.io/ 👉Repo github.com/handx-project/HandX

17 144

💥 GaussianGPT 3D GSC💥 👉From TUM, GaussianGPT: transformer-based 3D Gaussians generation via next-token prediction -> full 3D complex indoor scene. Repo announced💙 👉Review https://t.ly/bj-lL 👉Paper https://arxiv.org/pdf/2603.26661 👉Project https://nicolasvonluetzow.github.io/GaussianGPT/ 👉Repo TBA

17 144

🐍Pose-Appearance-Motion for HOI🐍 👉PAM is a novel Pose–Appearance–Motion Engine for controllable Hand–Object Interaction SOTA video generation. Repo/models available💙 👉Review 👉Paper arxiv.org/pdf/2603.22193 👉Project gasaiyu.github.io/PAM.github.io/ 👉Repo https://github.com/GasaiYU/PAM

17 144

🦪OccAny: Universal 3D Occupancy🦪 👉OccAny by Valeo is a novel unified framework for generalized unconstrained urban 3D occupancy prediction. Repo under Apache 2.0💙 👉Review 👉Paper https://arxiv.org/pdf/2603.23502 👉Project https://valeoai.github.io/OccAny/ 👉Repo https://github.com/valeoai/OccAny

17 144

🍓Material-Aware Grouping🍓 👉Material Magic Wand (Adobe) is a tool for material-aware grouping of parts in untextured 3D meshes. Given one selected part, it automatically retrieves the other parts in the same shape by its material. Repo announced💙 👉Review https://t.ly/q00SU 👉Paper https://arxiv.org/pdf/2603.17370 👉Project umangi-jain.github.io/material-magic-wand/ 👉Repo TBA

17 144

🍧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

17 144

🤖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

17 144

🌈 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

17 144

☄️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

17 144

🍓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

17 144

🔥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

17 144

📊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