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
🥛HAMSTER: Hierarchical VLA Manipulation🥛
👉#Nvidia unveils HAMSTER: novel Hierarchical VLA architecture to enable robotic manipulation with semantic, visual & geometric generalization trained on easy to collect, off-domain data. Source Code announced💙
👉Review https://t.ly/2yXaY
👉Paper https://arxiv.org/pdf/2502.05485
👉Project https://hamster-robot.github.io/
👉Repo TBA
🥛🥛HAMSTER: Hierarchical VLA Manipulation🥛🥛
👉#Nvidia unveils HAMSTER: novel Hierarchical VLA architecture to enable robotic manipulation with semantic, visual & geometric generalization trained on easy to collect, off-domain data. Source Code announced💙
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Hier. Action Models w/ SeparaTEd Path Represent.
✅Fine-tuned VLMs -> to low-level 3D policy models
✅A fully open-sourced enabler for VLM-action models
✅Abundant OOD data for improving real-world control
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse #LLM
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2502.05485
👉Project https://hamster-robot.github.io/
👉Repo TBA
🔮Flow-Based Foundation GenAI🔮
👉Goku is the novel SOTA family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. Amazing results! Repo released (now, empty)💙
👉Review https://t.ly/dzi0O
👉Paper http://arxiv.org/pdf/2502.04896
👉Project saiyan-world.github.io/goku/
👉Repo github.com/Saiyan-World/goku
💃HumanDiT Long-form Human💃
👉HumanDiT is a novel pose-guided Diffusion trained on a large and wild dataset w/ 14,000 hours of HQ video to produce HD videos with fine-grained bodies. Stunning results but no code announced🥲
👉Review https://t.ly/7rTRr
👉Paper https://arxiv.org/pdf/2502.04847
👉Project https://agnjason.github.io/HumanDiT-page/
🤖 META Human-Robot 🤖
👉#META PARTNR: novel benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration. The largest benchmark of its kind: 100,000+ natural language tasks, spanning 60 houses and 5,819 unique objects. Code & Data (🤗) under MIT💙
👉Review https://t.ly/zcN0K
👉Paper arxiv.org/pdf/2411.00081
👉Repo github.com/facebookresearch/partnr-planner
🤗Data huggingface.co/datasets/ai-habitat/partnr_episodes
👗3D Dynamic Garments👗
👉UCLA introduces Dress-1-to-3, a novel pipeline that reconstructs physics-plausible, simulation-ready separated garments with sewing patterns and humans from an in-the-wild image.
👉Review https://t.ly/qciHV
👉Paper arxiv.org/pdf/2502.03449
👉Project dress-1-to-3.github.io
🔥 VideoJAM: #META's Video-Model (SOTA) 🔥
👉#META's VideoJAM: the new SOTA (by large margin) in motion coherence for video generation, much better than SORA! A strong motion prior into any video-gen model. Impressive results, no code announced🥲
👉Review https://shorturl.at/id7Bt
👉Paper https://arxiv.org/pdf/2502.02492
👉Project https://hila-chefer.github.io/videojam-paper.github.io/
🛸Real-Time Differentiable Tracing🛸
👉 Radiant Foam is a novel scene representation by leveraging the decades-old efficient volumetric mesh ray tracing algorithm (largely overlooked in recent research). Performing like Gaussian Splatting, without the constraints of rasterization. Code announced💙
👉Review https://shorturl.at/26U06
👉Paper https://arxiv.org/pdf/2502.01157
👉Project https://radfoam.github.io/
👉Repo https://github.com/theialab/radfoam
🐙MambaGlue: SOTA feats. matching🐙
👉MambaGlue is a hybrid neural network combining the Mamba and the Transformer architectures to match local features. Source Code announced, to be released💙
👉Review https://shorturl.at/LxDG1
👉Paper arxiv.org/pdf/2502.00462
👉Repo https://lnkd.in/dAujfGZQ
🈯 SOTA 0-Shot Multi-View 🈯
👉MVGD by #TOYOTA is the SOTA method that generates images and scale-consistent depth maps from novel viewpoints given an arbitrary number of posed input views. A novel diffusion-based architecture capable of direct pixel-level generation. Code announced 💙
👉Review https://t.ly/_ecKl
👉Paper arxiv.org/pdf/2501.18804
👉Project mvgd.github.io/
👉Repo TBA
💎AI-driven Docs Conversion💎
👉Docling by IBM, is the ALL-in-ONE, open source solution for documents; parsing several types of popular formats into a unified, richly structured representation. Powered by SOTA models for layout (DocLayNet) and table structure (TableFormer), it runs efficiently on low-cost hardware. Code under MIT💙
👉Review https://t.ly/nSCfT
👉Paper https://lnkd.in/dc5Kpc2F
👉Repo https://lnkd.in/d9gvw9bt
Social feed of everyone is broken because of unnecessary/not required opinions about DeepSeek. Your wish:
🌅 Generative Human Mesh Recovery 🌅
👉GenHMR is a novel generative framework that reformulates monocular HMR as an image-conditioned generative task, explicitly modeling and mitigating uncertainties in 2D-to-3D mapping process. Impressive results but no code announced 🥺
👉Review https://t.ly/Rrzpj
👉Paper https://arxiv.org/pdf/2412.14444
👉Project m-usamasaleem.github.io/publication/GenHMR/GenHMR.html
☀️ Relightable Full-Body Avatars ☀️
👉#Meta unveils the first approach ever to jointly model the relightable appearance of the body, face, and hands of drivable avatars.
👉Review https://t.ly/kx9gf
👉Paper arxiv.org/pdf/2501.14726
👉Project neuralbodies.github.io/RFGCA
🦕[SOTA] Visual Grounding VOS🦕
👉ReferDINO is the first end-to-end approach for adapting foundational visual grounding models to RVOS. Code & models to be released soon💙
👉Review https://t.ly/SDFy9
👉Paper arxiv.org/pdf/2501.14607
👉Project isee-laboratory.github.io/ReferDINO/
👉Repo github.com/iSEE-Laboratory/ReferDINO
🎨MatAnyone: Human Matting🎨
👉MatAnyone is a novel approach for human video matting that supports the target assignment. Stable tracking in long videos even with complex/ambiguous BGs. Code & 🤗-Demo announced💙
👉Review https://t.ly/NVXsT
👉Paper arxiv.org/pdf/2501.14677
👉Project pq-yang.github.io/projects/MatAnyone
👉Repo TBA
🪆SOTA Points Segmentation🪆
👉VGG Oxford unveils a novel loss to segment objects in videos based on their motion and NO other forms of supervision! Training the net using long-term point trajectories as a supervisory signal to complement optical flow. New SOTA!
👉Review https://t.ly/8Bsbt
👉Paper https://arxiv.org/pdf/2501.12392
👉Code https://github.com/karazijal/lrtl
👉Project www.robots.ox.ac.uk/~vgg/research/lrtl/
🔥 The code of DynOMo is out 🔥
👉DynOMo is a novel model able to track any point in a dynamic scene over time through 3D reconstruction from monocular video: 2D and 3D point tracking from unposed monocular camera input
👉Review https://t.ly/t5pCf
👉Paper https://lnkd.in/dwhzz4_t
👉Repo github.com/dvl-tum/DynOMo
👉Project https://lnkd.in/dMyku2HW
🔥 The code of DynOMo is out 🔥
👉DynOMo is a novel model able to track any point in a dynamic scene over time through 3D reconstruction from monocular video: 2D and 3D point tracking from unposed monocular camera input. Source code released under BSD 3-Clause💙
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅TUM, CMU (H/T Jenny Seidenschwarz) & NVIDIA
✅Online 2D/3D point tracking from unposed monocular
✅Tracking-by-reconstruction baseline for online TAP
✅New baseline for online PT with unposed mono-cam
hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse hashtag#LLM
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://lnkd.in/dwhzz4_t
👉Repo github.com/dvl-tum/DynOMo
👉Project https://lnkd.in/dMyku2HW
🦠A-Life with Foundation Models🦠
👉A super team unveils ASAL, a new paradigm for Artificial Life research. A diverse range of ALife substrates including Boids, Particle Life, Game of Life, Lenia & Neural Cellular Automata. Code under Apache 2.0💙
👉Review https://t.ly/7SZ8A
👉Paper arxiv.org/pdf/2412.17799
👉Project http://pub.sakana.ai/asal/
👉Repo https://lnkd.in/dP5yxKtw
