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 238 Tecnologías y Aplicaciones7 725...

📈 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 17 173 suscriptores, ocupando la posición 7 725 en la categoría Tecnologías y Aplicaciones y el puesto 2 238 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 173 suscriptores.

Según los últimos datos del 19 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -177, y en las últimas 24 horas de -9, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 21.83%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 3 749 visualizaciones. En el primer día suele acumular 0 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 20 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.

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Suscriptores

-924 horas

-397 días

-17730 días

3 749

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

21.83%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

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Archivo de publicaciones

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💜MoRo: Human Motion Recovery💜 👉Masked modeling for human motion Recovery under Occlusions. Given a monocular video captured from a static camera, MoRo (by ETHZ & #Meta) robustly reconstructs accurate/physically plausible human motion, even under challenging occlusions. Repo released💙 👉Review https://t.ly/kK_je 👉Paper arxiv.org/pdf/2601.16079 👉Project mikeqzy.github.io/MoRo/ 👉Repo github.com/mikeqzy/MoRo

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🦧VideoMaMa: Mask-Guided Matting🦧 👉VideoMaMa is novel a diffusion-based model that converts binary segmentation masks into continuous alpha mattes. Repo, Dataset & Demo💙 👉Review https://t.ly/l_0f8 👉Paper arxiv.org/pdf/2601.14255 👉Project cvlab-kaist.github.io/VideoMaMa 👉Repo github.com/cvlab-kaist/VideoMaMa 👉Demo huggingface.co/spaces/SammyLim/VideoMaMa

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💊Foundation Medical SAM3 💊 👉Medical SAM3: foundation model for universal prompt-driven medical image segmentation, by fully fine-tuning SAM3 on large-scale, heterogeneous 2D/3D medical imaging datasets with paired segmentation masks-text prompts. Repo & Demo announced💙 👉Review https://t.ly/C6jcy 👉Paper https://arxiv.org/pdf/2601.10880 👉Project chongcongjiang.github.io/MedicalSAM3/# 👉Repo github.com/AIM-Research-Lab/Medical-SAM3

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💚 #META 3D Casual Captures 💚 👉#META unveils ShapeR, a novel approach for conditional 3D object shape generation from casually captured sequences. Impressive results. Repo under CC BY-NC 4.0💙 👉Review https://t.ly/j08sJ 👉Paper arxiv.org/pdf/2601.11514 👉Project facebookresearch.github.io/ShapeR/ 👉Repo github.com/facebookresearch/ShapeR

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👹SOTA Part-level Generator👹 👉A novel a text-to-motion model that learns to compose complex motions through hierarchical conditioning on part-, action- & sequence-level text, enabling fine-grained control over body parts & timing. Code, models & Dataset to be released💙 👉Review https://t.ly/leB_R 👉Paper arxiv.org/pdf/2601.10909 👉Project coral79.github.io/frankenmotion/ 👉Repo github.com/Coral79/FrankenMotion-Code

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💢3D Human Gen-Seg💢 👉CoMoVi takes an input image with a text description and generates 3D human motion & video sequence synchronously within a single diffusion denoising loop. Repo & Dataset releasing💙 👉Review https://t.ly/khSkm 👉Paper arxiv.org/pdf/2601.10632 👉Project igl-hkust.github.io/CoMoVi/ 👉Repo github.com/IGL-HKUST/CoMoVi 👉Data huggingface.co/datasets/AfterJourney/CoMoVi-Dataset

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💜Interactive Humanoid Generation💜 👉FlowAct-R1 by ByteDance is a novel framework that enables lifelike, responsive, and high-fidelity humanoid video generation for seamless real-time interaction. No code but impressive results (see video with audio) 💙 👉Review https://t.ly/aQhol 👉Paper arxiv.org/pdf/2601.10103 👉Project grisoon.github.io/FlowAct-R1/

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🍿100M Video Action Dataset🍿 👉Action100M by META is a large-scale dataset w/ 1.2M instructional videos (14.6 years of duration), yielding O(100M) temporally localized segments with open-vocabulary action supervision and rich captions. Repo under FAIR NC Research License💙 👉Review https://t.ly/w5KXe 👉Paper https://arxiv.org/pdf/2601.10592 👉Repo https://github.com/facebookresearch/Action100M

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🎇 Multi-target SAM3 🎇 👉SAM3-DMS is a novel training-free decoupled strategy that utilizes fine-grained memory selection on individual objects. Robust identity preservation and tracking stability. Repo under SAM License💙 👉Review https://t.ly/jJOAr 👉Paper https://arxiv.org/pdf/2601.09699 👉Repo https://github.com/FudanCVL/SAM3-DMS

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💚 Segment Anything w/ Geometry💚 👉3AM (NYCU + #Nvidia) offers cross-view correspondence even under large viewpoint changes, cluttered scenes, and variations in capture conditions, enabling robust object tracking from both videos & casual multi-view images. Repo (coming) & Demo available💙 👉Review https://t.ly/olZwE 👉Paper https://arxiv.org/pdf/2601.08831 👉Project https://jayisaking.github.io/3AM-Page/ 👉Repo https://github.com/jayisaking 👉Demo https://huggingface.co/spaces/nycu-cplab/3AM

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👉Games Workshop (Warhammer) is banning the use of AI in creative and design processes to protect IP and human creativity. A decision that goes against the current hype of widespread AI adoption. And what about your organization? I need your help👇 Vote: https://www.linkedin.com/posts/visionarynet_ai-activity-7417106327019196417-TpGL

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In 2026, who should we keep an eye on? Vote: https://www.linkedin.com/posts/visionarynet_ai-deeplearning-aiwithpapers-activity-7416886610795077632-qQeP/

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🫛Active Object Reconstruction🫛 👉ObjSplat (Beijing) autonomously plans viewpoints and progressively reconstructs an unknown object into a Hi-Fi Gaussian model and water-tight mesh, enabling direct use in physics simulations. Repo announced💙 👉Review https://t.ly/au6HE 👉Paper arxiv.org/pdf/2601.06997 👉Project li-yuetao.github.io/ObjSplat-page/ 👉Repo https://github.com/Li-Yuetao/ObjSplat

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🔥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

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🔥 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/

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🌍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

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🔥 Back from Holidays mood 🔥

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🦙 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

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⭐ 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💙

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