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

Malasia2 241 Tecnologías y Aplicaciones7 723...

📈 Análisis del canal de Telegram AI with Papers - Artificial Intelligence & Deep Learning

El canal AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 17 147 suscriptores, ocupando la posición 7 723 en la categoría Tecnologías y Aplicaciones y el puesto 2 241 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 147 suscriptores.

Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -190, y en las últimas 24 horas de -2, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 25.09%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 6.86% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 4 302 visualizaciones. En el primer día suele acumular 1 177 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 26.
Intereses temáticos: El contenido se centra en temas clave como framework, object, dataset, tba, depth.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

17 147

Suscriptores

-224 horas

-367 días

-19030 días

4 302

Visitas de la publicación

~ 1 17724 horas

~ 1 35348 horas

25.09%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 147

🎯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

17 147

🏜️ 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

17 147

🫠 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

17 147

👀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

17 147

💷 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

17 147

💚 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

17 147

🫎 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

17 147

🧱 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

17 147

🎷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

17 147

🐘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

17 147

✌️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

17 147

🦄 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

17 147

🥭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/

17 147

🌵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

17 147

🕶️ 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

17 147

🕶️ 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

17 147

🔥 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/

17 147

🍓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

17 147

🌩️ 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

17 147

🧪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