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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

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马来西亚2 130 技术与应用7 509...

📈 Telegram 频道 AI with Papers - Artificial Intelligence & Deep Learning 的分析概览

频道 AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) 英语语言赛道中的是活跃参与者。目前社区聚集了 17 261 名订阅者，在 技术与应用 类别中位列第 7 509，并在 马来西亚 地区排名第 2 130 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 17 261 名订阅者。

根据 25 十月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 261，过去 24 小时变化为 2，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 16.91%。内容发布后 24 小时内通常能获得 7.04% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 2 918 次浏览，首日通常累积 1 216 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 16。
主题关注点： 内容集中在 framework, object, dataset, tba, depth 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“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”

凭借高频更新（最新数据采集于 26 十月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

17 261

订阅者

+224 小时

+17 天

+26130 天

2 918

帖子浏览量

~ 1 21624 小时

~ 1 54948 小时

16.91%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

17 261

🌊 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/

17 261

🌐 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

17 261

🏝️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

17 261

🪷 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/

17 261

👗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

17 261

🌊 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

17 261

🎡 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

17 261

🌹 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

17 261

🛞 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

17 261

🪼 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

17 261

🎲 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

17 261

⚽ 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

17 261

🧤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)

17 261

🪐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

17 261

☄️ 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

17 261

⚛️ 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

17 261

🧞 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

17 261

🧞🧞 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

17 261

🔌 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

17 261

👗 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