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2 238 技术与应用7 725...

📈 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 17 173 名订阅者，在 技术与应用 类别中位列第 7 725，并在 马来西亚 地区排名第 2 238 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 17 173 名订阅者。

根据 19 六月, 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 -177，过去 24 小时变化为 -9，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 21.83%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 3 749 次浏览，首日通常累积 0 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 26。
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 20 六月, 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。

17 173

订阅者

-924 小时

-397 天

-17730 天

3 749

帖子浏览量

无数据24 小时

无数据48 小时

21.83%

参与率

无数据

每日帖子数

Ads index

beta

帖子存档

17 172

🎯Generative Refocusing is out🎯 👉Generative Refocusing is a two-step process that uses DeblurNet to recover all-in-focus images from various inputs and BokehNet for creating controllable bokeh (in semi-supervised mode). Repo under Apache2.0💙 👉Review https://t.ly/8t7PA 👉Paper arxiv.org/pdf/2512.16923 👉Project generative-refocusing.github.io/ 👉Repo github.com/rayray9999/Genfocus 👉Demo huggingface.co/spaces/nycu-cplab/Genfocus-Demo

17 172

🏜️ Depth Any Panoramas 🏜️ 👉DAP is the new SOTA foundation model for panoramic depth estimation with a large scale dataset. Data & Repo under MIT💙 👉Review https://t.ly/LaUmd 👉Paper arxiv.org/pdf/2512.16913 👉Project https://lnkd.in/dvqNV9jx 👉Repo https://lnkd.in/dmNzhb-7 👉Demo https://lnkd.in/dDwjMF3u

17 172

🫠 FlexAvatar: 3D Heads Avatars 🫠 👉TUM introduces FlexAvatar, a novel method for creating HQ and complete 3D head avatars from a single image. Code announced💙 👉Review https://t.ly/Rkdtd 👉Paper arxiv.org/pdf/2512.15599 👉Project tobias-kirschstein.github.io/flexavatar/ 👉Repo TBA

17 172

👀DriverGaze360: Driver SOTA👀 👉DriverGaze360 is a large-scale 360◦ field of view driver attention dataset, containing ∼1M gaze-labeled frames. Code & Dataset announced💙 👉Review https://t.ly/ZcoUw 👉Paper arxiv.org/pdf/2512.14266 👉Project av.dfki.de/drivergaze360/ 👉Repo github.com/dfki-av/drivergaze360 👉Data av.dfki.de/drivergaze360/dataset

17 172

💷 SOTA Zero-Shot Stereo Matching💷 👉Fast-FoundationStereo by #Nvidia is a novel family of architectures that achieve, for the first time, strong zero-shot generalization at real-time frame rate via divide-&-conquer acceleration. Code & Data announced💙 👉Review https://t.ly/XD6pO 👉Paper https://lnkd.in/d9_YKW2A 👉Project https://lnkd.in/dKDxm7EX 👉Repo https://lnkd.in/dR4-PdsW

17 172

💚 MatAnyone 2 is out! 💚 👉MatAnyone 2 is the most advanced human video matting framework that preserves fine details by avoiding segmentation-like boundaries, while also shows enhanced robustness under challenging real-world conditions. Repo & Dataset announced💙 👉Review https://www.linkedin.com/posts/visionarynet_artificialintelligence-ai-deeplearning-activity-7406244283055525888-8Dl_ 👉Paper arxiv.org/pdf/2512.11782 👉Project pq-yang.github.io/projects/MatAnyone2 👉Repo github.com/pq-yang/MatAnyone2

17 172

🫎 MoCapAnything is out 🫎 👉MoCapAnything is novel a reference-guided, factorized framework that first predicts 3D joint trajectories and then recovers asset-specific rotations via constraint-aware IK fitting. No code announced 🥲 👉Review https://t.ly/_Tw6t 👉Paper arxiv.org/pdf/2512.10881 👉Project animotionlab.github.io/MoCapAnything

17 172

🧱 Pixel Art Volumetric Rendering 🧱 👉Voxify3D is a novel differentiable two-stage framework bridging 3D mesh optimization with 2D pixel art supervision. Repo announced💙 👉Review https://t.ly/qPyNl 👉Paper https://lnkd.in/du5ikJGN 👉Project https://lnkd.in/dpiAjj5m 👉Repo TBA

17 172

🎷Layered PSD Diffusion🎷 👉OmniPSD produces layered PSD files with transparent alpha channels, separating text, foreground elements, and background into clean RGBA layers that can be directly edited in tools. Online Demo💙 👉Review https://t.ly/YNRAC 👉Paper arxiv.org/pdf/2512.09247 👉Project showlab.github.io/OmniPSD/ 👉Demo https://www.lovart.ai/it

17 172

🐘TTSC for 3D Generative🐘 👉SpaceControl is the new SOTA training-free test-time method for explicit spatial control of 3D generation. Repo announced💙 👉Review https://t.ly/1zrah 👉Paper https://lnkd.in/dEWh3vep 👉Project https://lnkd.in/dScftUmm 👉Repo TBA

17 172

✌️SOTA Generative SLP✌️ 👉Stable Signer is a new sign language generative model. It redefines the SLP task as a hierarchical generation end-to-end task that only includes text understanding (Prompt2Gloss, Text2Gloss) and Pose2Vid. Repo with data 💙 👉Review https://t.ly/yKZhn 👉Paper arxiv.org/pdf/2512.04048 👉Project stablesigner.github.io/ 👉Data github.com/SignLLM/Prompt2Sign/tree/main/tools-new-2025

17 172

🦄 Native Unified Multimodal 🦄 👉META unveils a novel UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows E2E processing of images/videos for both understanding/generation. Code under legal review💙 👉Review https://t.ly/7wmKP 👉Paper https://lnkd.in/djT4WGEU 👉Project https://tuna-ai.org/ 👉Repo github.com/wren93/tuna

17 172

🥭3D Point Motion Editing🥭 👉Edit-by-Track enables precise video motion editing via 3D point tracks. By specifying desired 3D trajectories, users can seamlessly control joint camera and object motion, remove objects, and transfer motion between videos. No code announced but it's a relevant paper💙 👉Review https://t.ly/lqzHY 👉Paper https://arxiv.org/pdf/2512.02015 👉Project https://edit-by-track.github.io/

17 172

🌵Instance-Level Video Generation🌵 👉InstanceV is the first video generation framework to be designed specifically for instance-level control at the architectural level. Code & Data announced💙 👉Review https://t.ly/y_TBT 👉Paper arxiv.org/pdf/2511.23146 👉Project aliothchen.github.io/projects/InstanceV/ 👉Repo TBA

17 172

🕶️ Seeing without Pixels 🕶️ 👉Is it possible to perceive a video’s content without seeing its pixels, just from the camera trajectory? Deepmind (+ UTexas) is the first to systematically investigate this seemingly implausible question💙 👉Review https://t.ly/Ymd1c 👉Paper arxiv.org/pdf/2511.21681 👉Project sites.google.com/view/seeing-without-pixels

17 172

🕶️ Seeing without Pixels 🕶️ 👉Is it possible to perceive a video’s content without seeing its pixels, just from the camera trajectory? #Google Deepmind (+ UTexas) is the first to systematically investigate this seemingly implausible question💙 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅CamFormer: pre-trained dedicated trajectory encoder ✅Proposing a novel contextualized trajectory encoding ✅Scenarios: egocentric & exocentric (third-person) ✅Outperforming "heavy" vision models in key scenarios #artificialintelligence #AI #deeplearning #AIwithPapers #LLM 👉Discussion https://lnkd.in/dMgakzWm 👉Paper https://arxiv.org/pdf/2511.21681 👉Project https://sites.google.com/view/seeing-without-pixels

17 172

🔥 Smell Like Vision Spirit 🔥 👉New York Smells is a novel large-scale dataset of paired vision and olfaction captured in-the-wild, enabling the new task of cross-modal learning between smell and sight. With the lights out, it's less dangerous. Dataset available💙 👉Review https://t.ly/Ycn_B 👉Paper arxiv.org/pdf/2511.20544 👉Project smell.cs.columbia.edu/

17 172

🍓MotionV2V: Editing Motion in Video🍓 👉 Google unveils motion edits, a new approach for editing videos by controlling the change in motion from the original to the edited video using diffusion models. Impressive results. Repo released soon💙 👉Review https://t.ly/s0sIT 👉Paper https://arxiv.org/pdf/2511.20640 👉Project https://ryanndagreat.github.io/MotionV2V/ 👉Repo https://github.com/RyannDaGreat/MotionV2V

17 172

🌩️ Cloud4D in time 🌩️ 👉Cloud4D: physically-realistic 3D cloud fields using ground-based cameras at a 25 m spatial resolution and 5 s temporal resolution. Repo coming, Data released💙 👉Review https://t.ly/w7Zly 👉Paper arxiv.org/pdf/2511.19431 👉Project cloud4d.jacob-lin.com/ 👉Repo TBD 👉Data https://drive.google.com/drive/folders/1QU_0kIUXIVt8h3uqygBeaF3Gvr_L5SdX?usp=drive_link

17 172

🧪EfficientSAM3 is out 🧪 👉Bristol announces EfficientSAM3, a family of efficient models built on Progressive Hierarchical Distillation (PHD) that transfers capability from SAM3 to lightweight students. Code coming (in sync with SAM3 release)💙 👉Review https://t.ly/bfXP2 👉Paper https://arxiv.org/pdf/2511.15833 👉Project https://simonzeng7108.github.io/efficientsam3/ 👉Repo https://github.com/SimonZeng7108/efficientsam3