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
Mostrar más📈 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 17 021 suscriptores, ocupando la posición 7 494 en la categoría Tecnologías y Aplicaciones y el puesto 2 177 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 021 suscriptores.
Según los últimos datos del 25 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -24, y en las últimas 24 horas de 10, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 22.06%. 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 0 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 0.
- 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 26 agosto, 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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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
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Publicaciones del Canal
🍋🟩Remesh-Aware Mesh Deformation🍋🟩
👉RADmesh is a novel generative deformation technique enhanced by remeshing. Given a text prompt, it deforms and remeshes a mesh region to form new geometric features. Repo MIT💙
👉Review https://lnkd.in/p/eK6FZv9c
👉Paper https://arxiv.org/pdf/2608.17182
👉Project https://threedle.github.io/radmesh/
👉Repo https://github.com/threedle/radmesh/
| 2 |  🐆 Anyone in 4D is out 🐆
👉4DAnyone turns a casual monocular video into multi-view videos, enabling downstream 4DGS reconstruction. Full repo under Apache 2.0💙
👉Review https://lnkd.in/p/ec4dzGvb
👉Paper https://arxiv.org/pdf/2608.20335
👉Project https://4danyone.github.io
👉Repo github.com/ant-research/4DAnyone | 1 425 |
| 3 |  🔥🔥UPAL: Unified Points n' Lines🔥🔥
👉ETH (+Microsoft Spatial AI Lab) unveils a novel feature extractor that jointly extracts keypoints, lines, and feature descriptors within a single lightweight net. SOTA in line detection can be achieved by adding only three convolutional layers to existing point extractor. Repo under Apache💙
👉Review https://lnkd.in/p/eW8j5JZj
👉Paper https://arxiv.org/pdf/2608.19894
👉Repo https://github.com/francois141/upal | 1 689 |
| 4 |  🐠Dual-branch Elasticity ID-Tracking🐠
👉TIDE: tracking dense, homogeneous targets, providing a scalable dual-branch design to accommodate diverse hardware constraints. MIT license💙
👉Review https://t.ly/WEDeY
👉Paper https://arxiv.org/pdf/2607.26412
👉Project https://vranlee.github.io/TIDE/
👉Repo https://github.com/vranlee/TIDE | 4 280 |
| 5 |  🔥Decoder-only Any-to-Any Model🔥
👉MODUS unifies any-to-any multimodal generation with one decoder, two experts, and zero task heads. Impressive work. Repo under Apache💙
👉Review https://t.ly/-2QKT
👉Paper https://lnkd.in/dhfBQGhB
👉Project https://lnkd.in/dPD_ECXk
👉Repo https://lnkd.in/dbDHw24u | 3 896 |
| 6 |  🍿 Dawn of Generative Cinematography 🍿
🟩 #TheOdyssey by Christopher Nolan was shot entirely on IMAX 70mm. It feels almost romantic: massive cameras, film stock, premium lenses, and an obsessive pursuit of the highest possible quality at the moment of capture.
👉 Meanwhile, #AI research is heading in the exact opposite direction.
🟩 A pre-print paper released today, "Camera Anything", demonstrates something that sounded like science fiction just a few years ago: you film a scene once... and then you can virtually reposition the camera anywhere.
👉More https://t.ly/Kd7RV
👉Paper arxiv.org/pdf/2607.24591
👉Project yixuanli98.github.io/cameraanything/
👉Repo github.com/yixuanli98/CameraAnything | 3 758 |
| 7 | 🍿Dawn of Generative Cinematography🍿
🟩 The Odissey by Christopher Nolan was shot entirely on IMAX 70mm. It feels almost romantic: massive cameras, film stock, premium lenses, and an obsessive pursuit of the highest possible quality at the moment of capture.
👉 Meanwhile, #AI research is heading in the exact opposite direction.
🟩 A pre-print paper released today, "Camera Anything", demonstrates something that sounded like science fiction just a few years ago: you film a scene once... and then you can virtually reposition the camera anywhere.
🟩 Want a close-up? A drone shot? A ground-level perspective? A side angle? A cinematic tracking shot? You no longer decide where to place the camera during filming. You decide afterwards.
👉 And this fundamentally changes what cinematography means.
🟩 For more than a century, filmmakers have had to make irreversible decisions on set. Camera placement, focal length, movement, framing, etc. These choices became part of the recorded footage forever.
🟩 A scene becomes a 3D representation that can be "re-shot" endlessly from viewpoints that never physically existed. We simply capture "raw" data from which the final result is reconstructed or customized.
🟩Five years from now, will we still talk about shooting a movie? Or will we simply capture a scene and decide later where the camera should have been?
The irony is fascinating. While Nolan reminds us how extraordinary a 70mm camera can be, AI is quietly suggesting that, soon, the camera itself will be optional.
👉The first step towards the generative cinematography.
#deeplearning #computervision #AIwithPapers
👉Discussion https://lnkd.in/dMgakzWm
👉Paper arxiv.org/pdf/2607.24591
👉Project yixuanli98.github.io/cameraanything/
👉Repo github.com/yixuanli98/CameraAnything | 1 |
| 8 | 🍿🍿Dawn of Generative Cinematography🍿🍿
🟩#TheOdyssey by Christopher Nolan was shot entirely on IMAX 70mm. It feels almost romantic: massive cameras, film stock, premium lenses, and an obsessive pursuit of the highest possible quality at the moment of capture.
👉Meanwhile, #AI research is heading in the exact opposite direction.
🟩A pre-print paper released today, "Camera Anything", demonstrates something that sounded like science fiction just a few years ago: you film a scene once... and then you can virtually reposition the camera anywhere.
🟩Want a close-up? A drone shot? A ground-level perspective? A side angle? A cinematic tracking shot? You no longer decide where to place the camera during filming. You decide afterwards.
👉And this fundamentally changes what cinematography means.
🟩For more than a century, filmmakers have had to make irreversible decisions on set. Camera placement, focal length, movement, framing, etc. These choices became part of the recorded footage forever.
🟩A scene becomes a 3D representation that can be "re-shot" endlessly from viewpoints that never physically existed. We simply capture "raw" data from which the final result is reconstructed or customized.
🟩Five years from now, will we still talk about shooting a movie? Or will we simply capture a scene and decide later where the camera should have been?
The irony is fascinating. While Nolan reminds us how extraordinary a 70mm camera can be, AI is quietly suggesting that, soon, the camera itself will be optional.
👉The first step towards the generative cinematography.
#deeplearning #computervision #AIwithPapers
👉Discussion https://lnkd.in/dMgakzWm
👉Paper arxiv.org/pdf/2607.24591
👉Project yixuanli98.github.io/cameraanything/
👉Repo github.com/yixuanli98/CameraAnything | 2 |
| 9 |  🔎MicroZoom at Extreme Scale🔎
👉MicroZoom by UWA synthesizes gigapixel-resolution images grounded in consumer-grade microscope close-ups at magnification levels up to 350×. Impressive. Repo under MIT💙
👉Review https://t.ly/hgJD7
👉Paper https://arxiv.org/pdf/2607.24729
👉Project https://microzoom-sr.github.io/
👉Repo github.com/MicroZoom-SR/MicroZoom-SR.github.io | 2 953 |
| 10 |  💄MagicMakeup Makeup-Transfer💄
👉Makeup-transfer applies the reference makeup to the source face while preserving the source identity. Authors: Zhejiang University & vivo BlueImage Lab. Repo for non commercial💙
👉Review https://t.ly/JYpCr
👉Paper https://arxiv.org/pdf/2607.20924
👉Project https://vivocameraresearch.github.io/magicmakeup/
👉Repo https://github.com/vivoCameraResearch/Magic-Makeup | 2 898 |
| 11 |  💢Unified Video Dense Prediction💢
👉UniD (Adobe Research + Cornell University) is a novel unified video model that jointly predicts: depth, surface normals, semantic segmentation, boundaries, human parts, albedo, shading, and materials. Code TBR💙
👉Review https://t.ly/oo7et
👉Paper https://arxiv.org/pdf/2607.21592
👉Project https://unid-video.github.io/
👉Repo https://github.com/YihongSun/UniD | 3 145 |
| 12 |  🫛Spatially-Aware Class-Agnostic Counting🫛
👉UpCount is reference-free, spatially aware, class-agnostic object counting with an MAE-pretrained ViT, DPT-style feature reassembly, FeatUp-style joint bilateral upsampling, and proposal verification. Repo under MIT💙
👉Review https://t.ly/dWOc3
👉Paper https://arxiv.org/pdf/2607.16826
👉Repo github.com/r28112072-rgb/upcount | 3 264 |
| 13 |  🦜Streaming 4D Transformer🦜
👉IGGT4D is a novel a streaming instance-grounded geometry transformer for online 4D scene understanding. It processes video frames sequentially, reuses historical context through causal spatial-temporal modeling, and incrementally updates a unified representation of camera motion, geometry, and object identity. Repo/Data announced💙
👉Review https://t.ly/LFrKR
👉Paper https://arxiv.org/pdf/2607.19228
👉Project https://iggt4d.github.io/
👉Repo TBA | 3 123 |
| 14 | What about more posts about Robotics? | 2 947 |
| 15 |  👉Not a render. Not a concept. This is GENE.01 by Generative Bionics, the Italians coolest scaleup strikes back: in just six months, they turned GENE.01 into a fully functional humanoid platform that can walk, sense and interact.
👉Full-body multimodal skin perceives touch, proximity, force and temperature, bringing Physical AI closer to safe and natural collaboration with people.
👉More: https://t.ly/F3I3A | 2 883 |
| 16 |  🏯SOTA Music-to-Dance Gen🏯
👉The Tongyi Lab unveils Wan-Dancer, a novel stable minute-scale synthesis at 720p/30fps across five dance genres. Impressive results, new SOTA on long clip by a large margin. Repo under Apache 2.0💙
👉Review https://t.ly/AKY5j
👉Paper https://lnkd.in/d_xA7dwb
👉Project https://lnkd.in/dzfnw2h4
👉Repo https://lnkd.in/d-Zj_cTf | 3 663 |
| 17 |  🌈FlowWAM: flow->action prediction🌈
👉FlowWAM is a novel dual-stream diffusion framework that adopts optical flow as a unified, video-native action representation. Repo under Apache💙
👉Review https://t.ly/FmutT
👉Paper https://arxiv.org/abs/2607.13017
👉Project https://flow-wam.github.io/
👉Repo github.com/YixiangChen515/FlowWAM | 3 930 |
| 18 |  🦧 MonkeyOCRv2 is out! 🦧
👉MonkeyOCRv2 is a text-centric visual foundation model that unifies fine-grained text modeling, cross-task representation learning, and cross-lingual generalization in a single encoder. Released for academic research and non-commercial use💙
👉Review https://t.ly/yicEK
👉Paper https://arxiv.org/pdf/2607.11562
👉Repo https://github.com/Yuliang-Liu/MonkeyOCRv2 | 3 887 |
| 19 |  🎂REMIND: long-term MOT re-ID🎂
👉REMIND by CVAR-UPM is a novel online tracker designed for long-term multi-object re-ID of generic indoor objects from monocular RGB, requiring neither camera pose nor depth. Repo under MIT💙
👉Review https://t.ly/AkQoI
👉Paper https://lnkd.in/dm58mkCv
👉Project https://lnkd.in/dZrAZqFe
👉Repo https://lnkd.in/dbidrwxU | 3 589 |
| 20 |  🌔Foundation Global SFM🌔
👉Glob3R is a global SfM-style reconstruction built on 3D foundation models. key idea: explicitly optimize feed-forward geometric predictions. Repo TBA💙
👉Review https://t.ly/Z_4C7
👉Paper https://arxiv.org/pdf/2607.09225
👉Project https://junyuandeng.github.io/Glob3r/
👉Repo TBA | 3 509 |
