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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

Malasia2 130 Tecnologías y Aplicaciones7 497...

📈 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 17 261 suscriptores, ocupando la posición 7 497 en la categoría Tecnologías y Aplicaciones y el puesto 2 130 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 261 suscriptores.

Según los últimos datos del 22 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 253, y en las últimas 24 horas de -1, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 18.98%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.07% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 3 275 visualizaciones. En el primer día suele acumular 1 220 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 23.
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 23 octubre, 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.

17 261

Suscriptores

-124 horas

+67 días

+25330 días

3 275

Visitas de la publicación

~ 1 22024 horas

~ 1 52548 horas

18.98%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

17 262

🔥Orient Anything V2 is out🔥 👉Orient Anything V2 is a foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Repo under CC-BY-4.0💙 👉Review https://t.ly/Ht7Xd 👉Paper arxiv.org/pdf/2601.05573 👉Project orient-anythingv2.github.io/ 👉Repo github.com/SpatialVision/Orient-Anything-V2

17 262

🔥 New #AI Startups in 2026? 🔥 In 2026, which area would you focus on? 🤖Agents → workflows, copilots, etc. 🏭Vertical AI → Pharma, Automotive, Energy ... 🧠Infrastructure → MLOps, Security, Cost Control ... 🎨AI for Creators/Media → Video, avatars, contents ... Please, help me understanding what's next with this poll on LinkedIn :) https://www.linkedin.com/posts/visionarynet_ai-ai-deeplearning-activity-7415377341779996672-sQO1 LUV U \m/

17 262

🌍Label Any Object in 3D 🌍 👉LabelAny3D: novel analysis-by-synthesis framework that reconstructs holistic 3D scenes from 2D to efficiently produce HQ 3D BBs annotations. Repo under CC-BY-4.0 license💙 👉Review https://t.ly/bO93j 👉Paper https://lnkd.in/dYb97zWG 👉Project https://lnkd.in/dJ9UKERb 👉Repo https://lnkd.in/d9SxtmiA

17 262

🔥 Back from Holidays mood 🔥

17 262

🦙 Depth as Neural Implicit 🦙 👉InfiniDepth represents depth as neural implicit fields, "infinite" (i.e.16K) resolution and geometrical details. Repo under Apache 2.0💙 👉Review https://t.ly/4we5t 👉Paper https://lnkd.in/dpiHQExj 👉Project https://lnkd.in/dy3JxKye 👉Repo https://lnkd.in/dAXbnK5z

17 262

⭐ TOP 5 Papers you loved in 2025 ⭐ 👉 In 2025 novel architectures have redefined efficiency and accuracy, and almost every day brought a new SOTA in image understanding, tracking, and #GenAI. It’s been an inspiring ride, and 2026 it will be even wilder. This community (LinkedIn + Telegram) is now around 80,000+ people. 𝐏𝐚𝐩𝐞𝐫𝐬 (𝐛𝐲 𝐲𝐨𝐮𝐫 𝐩𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞): ⭐3D LLM Understanding https://t.ly/ejr1s ⭐DynOMo is out https://t.ly/t5pCf ⭐Tracking Transformations https://t.ly/NPyW4 ⭐YOLOv12 (new SOTA) https://t.ly/jj1oR ⭐Gaussian Surface Tracking https://t.ly/udpMq Thank you all💙

17 262

🎯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

17 262

🏜️ 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

17 262

🫠 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

17 262

👀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

17 262

💷 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

17 262

💚 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

17 262

🫎 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

17 262

🧱 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

17 262

🎷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

17 262

🐘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

17 262

✌️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

17 262

🦄 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

17 262

🥭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/