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
🌊 Diffusive 3D Human Recovery 🌊
👉The Rutgers University unveils ScoreHMR at #CVPR24; novel approach for 3D human pose and shape reconstruction. Impressive results.
👉Review https://t.ly/G0k2D
👉Paper https://arxiv.org/pdf/2403.09623
👉Code https://github.com/statho/ScoreHMR
👉Project https://statho.github.io/ScoreHMR/
🌐 3D Scenes with Depth Inpainting 🌐
👉Oxford announced two novel contributions to the field of 3D scene generation: a new benchmark and a novel depth completion model. 🤗-Demo and Source Code released💙
👉Review https://t.ly/BKiny
👉Paper https://arxiv.org/pdf/2404.19758
👉Project https://research.paulengstler.com/invisible-stitch/
👉Code https://github.com/paulengstler/invisible-stitch
👉Demo https://huggingface.co/spaces/paulengstler/invisible-stitch
🏝️1000x Scalable Neural 3D Fields🏝️
👉Highly-scalable neural 3D Fields: 1000x reductions in memory maintaining speed/quality: 10 MB vs. 10 GB! Code released 💙
👉Review https://t.ly/sLTK5
👉Paper https://lnkd.in/dEYM8-t2
👉Project https://lnkd.in/djptdujx
👉Code https://lnkd.in/dcCnFZ2n
🪷 Tunnel Try-on: SOTA VTON 🪷
👉"Tunnel Try-on", the first diffusion-based video virtual try-on model that demonstrates SOTA performance in complex scenarios. No code announced :(
👉Review https://t.ly/joMtJ
👉Paper arxiv.org/pdf/2404.17571
👉Project mengtingchen.github.io/tunnel-try-on-page/
👗TELA: Text to 3D Clothed Human👗
👉 TELA is a novel approach for the new task of clothing disentangled 3D human model generation from texts. This novel approach unleashes the potential of many downstream applications (e.g., virtual try-on).
👉Review https://t.ly/6N7JV
👉Paper https://arxiv.org/pdf/2404.16748
👉Project https://jtdong.com/tela_layer/
👉Code https://github.com/DongJT1996/TELA
🌊 FlowMap: dense depth video 🌊
👉MIT (+CSAIL) unveils FlowMap, a novel E2E differentiable method that solves for precise camera poses, camera intrinsics, and perframe dense depth of a video sequence. Source Code released 💙
👉Review https://lnkd.in/dMgakzWm
👉Paper arxiv.org/pdf/2404.15259.pdf
👉Project cameronosmith.github.io/flowmap
👉Code github.com/dcharatan/flowmap
🎡 NER-Net: Seeing at Nighttime 🎡
👉Huazhong (+Beijing) unveils a novel event-based nighttime imaging solution under non-uniform illumination, plus a paired multi-illumination level real-world dataset. Repo online, code coming 💙
👉Review https://t.ly/Z9JMJ
👉Paper arxiv.org/pdf/2404.11884.pdf
👉Repo github.com/Liu-haoyue/NER-Net
👉Clip https://www.youtube.com/watch?v=zpfTLCF1Kw4
🌹 Physics-Based 3D Video-Gen 🌹
👉PhysDreamer, a physics-based approach that leverages the object dynamics priors learned by video generation models. It enables realistic 3D interaction with objects
👉Review https://shorturl.at/bivP4
👉Paper https://arxiv.org/pdf/2404.13026.pdf
👉Project https://physdreamer.github.io/
👉Code github.com/a1600012888/PhysDreamer
🛞 6Img-to-3D driving scenarios 🛞
👉EPFL (+ Continental) unveils 6Img-to-3D, novel transformer-based encoder-renderer method to create 3D onbounded outdoor driving scenarios with only six pics
👉Review https://shorturl.at/dZ018
👉Paper arxiv.org/pdf/2404.12378.pdf
👉Project 6img-to-3d.github.io/
👉Code github.com/continental/6Img-to-3D
🪼 All You Need is SAM (+Flow) 🪼
👉Oxford unveils the new SOTA for moving object segmentation via SAM + Optical Flow. Two novel models & Source Code announced 💙
👉Review https://t.ly/ZRYtp
👉Paper https://lnkd.in/d4XqkEGF
👉Repo coming
👉Project https://lnkd.in/dHpmx3FF
🎲 Articulated Objs from MonoClips 🎲
👉REACTO is the new SOTA to address the challenge of reconstructing general articulated 3D objects from single monocular video
👉Review https://t.ly/REuM8
👉Paper https://lnkd.in/d6PWagij
👉Project https://lnkd.in/dpg3x4tm
👉Repo https://lnkd.in/dRZWj6_N
⚽ SoccerNET: Athlete Tracking & ID ⚽
👉SoccerNet Challenge is a novel high level computer vision task that is specific to sports analytics. It aims at recognizing the state of a sport game, i.e., identifying and localizing all sports individuals (players, referees, ..) on the field.
👉Review https://t.ly/Mdu9s
👉Paper arxiv.org/pdf/2404.11335.pdf
👉Code github.com/SoccerNet/sn-gamestate
🧤Neural MusculoSkeletal-MANO🧤
👉SJTU unveils MusculoSkeletal-MANO, novel musculoskeletal system with a learnable parametric hand model. Source Code announced 💙
👉Review https://lnkd.in/dMgakzWm
👉Paper arxiv.org/pdf/2404.10227.pdf
👉Project https://ms-mano.robotflow.ai/
👉Code announced (no repo yet)
🪐YOLO-CIANNA: Neural Astro🪐
👉 CIANNA is a general-purpose deep learning framework for (but not only for) astronomical data analysis. Source Code released 💙
👉Review https://t.ly/441XS
👉Paper arxiv.org/pdf/2402.05925.pdf
👉Code github.com/Deyht/CIANNA
👉Wiki github.com/Deyht/CIANNA/wiki
☄️ Tracking Any 2D Pixels in 3D ☄️
👉 SpatialTracker lifts 2D pixels to 3D using monocular depth, represents the 3D content of each frame efficiently using a triplane representation, and performs iterative updates using a transformer to estimate 3D trajectories.
👉Review https://t.ly/B28Cj
👉Paper https://lnkd.in/d8ers_nm
👉Project https://lnkd.in/deHjtZuE
👉Code https://lnkd.in/dMe3TvFT
⚛️ Flying w/ Photons: Neural Render ⚛️
👉Novel neural rendering technique that seeks to synthesize videos of light propagating through a scene from novel, moving camera viewpoints. Pico-Seconds time resolution!
👉Review https://t.ly/ZqL3a
👉Paper arxiv.org/pdf/2404.06493.pdf
👉Project anaghmalik.com/FlyingWithPhotons/
👉Code github.com/anaghmalik/FlyingWithPhotons
🧞 XComposer2: 4K Vision-Language 🧞
👉InternLMXComposer2-4KHD brings LVLM resolution capabilities up to 4K HD (3840×1600) and beyond. Authors: Shanghai AI Lab, CUHK, SenseTime & Tsinghua. Source Code & Models released 💙
👉Review https://t.ly/GCHsz
👉Paper arxiv.org/pdf/2404.06512.pdf
👉Code github.com/InternLM/InternLM-XComposer
🧞🧞 XComposer2-4K: 4K Vision-Language 🧞🧞
👉InternLMXComposer2-4KHD brings LVLM resolution capabilities up to 4K HD (3840×1600) and beyond. Authors: Shanghai AI Lab, CUHK, SenseTime & Tsinghua. Source Code & Models released 💙
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Large Vision-Language Models (LVLMs) to 4K HD
✅Free-form Interleaved Text-Image Composition
✅Dynamic Resolution / Automatic Patch Config.
✅SOTA or competitive despite only 7B params
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Discussion https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2404.06512.pdf
👉Code github.com/InternLM/InternLM-XComposer
🔌 BodyMAP: human body & pressure 🔌
👉#Nvidia (+CMU) unveils BodyMAP, the new SOTA in predicting body mesh (3D pose & shape) and 3D applied pressure on the human body. Source Code released, Dataset coming 💙
👉Review https://t.ly/8926S
👉Project bodymap3d.github.io/
👉Paper https://lnkd.in/gCxH4ev3
👉Code https://lnkd.in/gaifdy3q
👗 Neural Bodies with Clothes 👗
👉Neural-ABC is a novel parametric model based on neural implicit functions that can represent clothed human bodies with disentangled latent spaces for identity, clothing, shape, and pose. Author: University of Science & Technology of China. Dataset & Source Code released 💙
👉Review https://t.ly/Un1wc
👉Project https://lnkd.in/dhDG6FF5
👉Paper https://lnkd.in/dhcfK7jZ
👉Code https://lnkd.in/dQvXWysP
