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 17 053 suscriptores, ocupando la posición 7 643 en la categoría Tecnologías y Aplicaciones y el puesto 2 199 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 053 suscriptores.
Según los últimos datos del 11 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -159, y en las últimas 24 horas de -4, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 17.11%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.59% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 917 visualizaciones. En el primer día suele acumular 1 295 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 15.
- 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 12 julio, 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.
17 053
Suscriptores
-424 horas
-497 días
-15930 días
Archivo de publicaciones
💥 GaussianGPT 3D GSC💥
👉From TUM, GaussianGPT: transformer-based 3D Gaussians generation via next-token prediction -> full 3D complex indoor scene. Repo announced💙
👉Review https://t.ly/bj-lL
👉Paper https://arxiv.org/pdf/2603.26661
👉Project https://nicolasvonluetzow.github.io/GaussianGPT/
👉Repo TBA
🐍Pose-Appearance-Motion for HOI🐍
👉PAM is a novel Pose–Appearance–Motion Engine for controllable Hand–Object Interaction SOTA video generation. Repo/models available💙
👉Review
👉Paper arxiv.org/pdf/2603.22193
👉Project gasaiyu.github.io/PAM.github.io/
👉Repo https://github.com/GasaiYU/PAM
🦪OccAny: Universal 3D Occupancy🦪
👉OccAny by Valeo is a novel unified framework for generalized unconstrained urban 3D occupancy prediction. Repo under Apache 2.0💙
👉Review
👉Paper https://arxiv.org/pdf/2603.23502
👉Project https://valeoai.github.io/OccAny/
👉Repo https://github.com/valeoai/OccAny
🍓Material-Aware Grouping🍓
👉Material Magic Wand (Adobe) is a tool for material-aware grouping of parts in untextured 3D meshes. Given one selected part, it automatically retrieves the other parts in the same shape by its material. Repo announced💙
👉Review https://t.ly/q00SU
👉Paper https://arxiv.org/pdf/2603.17370
👉Project umangi-jain.github.io/material-magic-wand/
👉Repo TBA
🍧10,000× faster SAM-3D🍧
👉Fast SAM 3D Body achieves up to 10.9× speedup, over 10,000× faster MHR-to-SMPL conversion -> real-time humanoid control from RGB. Repo available💙
👉Review https://t.ly/uHx84
👉Paper https://arxiv.org/pdf/2603.15603
👉Project yangtiming.github.io/Fast-SAM-3D-Body-Page/
👉Repo https://github.com/yangtiming/Fast-SAM-3D-Body
🤖Physically-Plausible Human🤖
👉PhysMoDPO is a novel direct preference optimization framework for humanoid motion generation. Repo under MIT💙
👉Review https://t.ly/clf8w
👉Paper https://arxiv.org/pdf/2603.13228
👉Project https://mael-zys.github.io/PhysMoDPO/
👉Repo https://github.com/Mael-zys/PhysMoDPO
🌈 New SOTA Video Depth 🌈
👉DVD is the new Video Depth Estimation SOTA with full training suite available under Apache2.0💙
👉Review https://t.ly/gpCkG
👉Paper https://arxiv.org/pdf/2603.12250
👉Project https://dvd-project.github.io/
👉Repo github.com/EnVision-Research/DVD
☄️OmniStream: Perceive-Reconstruct-Act ☄️
👉Novel unified streaming visual backbone that effectively perceives, reconstructs, and acts from diverse visual inputs. Repo/Models announced💙
👉Review https://t.ly/_zZMO
👉Paper arxiv.org/pdf/2603.12265
👉Project go2heart.github.io/omnistream/
👉Repo github.com/Go2Heart/OmniStream
🍓Surface Light Tokenizer🍓
👉Apple unveils LITO a novel latent flow matching model enables HQ image-to-3D. Latent representation that encodes a surface light field into a compact set of latent vectors. Impressive results but no code🥲
👉Review https://t.ly/xcWNe
👉Paper https://lnkd.in/dYHwY4YX
👉Project https://lnkd.in/dtJT8bXy
🔥Holistic 3D Spatial Intelligence🔥
👉Holi-Spatial is the first fully automated pipeline capable of converting raw video streams into holistic 3D spatial annotations without human intervention. Code/Data announced💙
👉Review https://t.ly/PDpr9
👉Paper https://lnkd.in/dTbMuZCm
👉Project https://lnkd.in/d66CYB4q
👉Repo https://lnkd.in/dAGzShXj
📊Real-Time Scene Graph📊
👉REACT++ by Umea University is the new state-of-the-art model for real-time SGG: 20% faster with a gain of 10% in relation prediction accuracy on average. Code under MIT💙
👉Review https://t.ly/c12VX
👉Paper https://arxiv.org/pdf/2603.06386
👉Repo https://github.com/Maelic/SGG-Benchmark
🎪SOTA Arbitrary Tracking🎪
👉TAPFormer is the novel SOTA transformer-based framework that performs asynchronous temporal-consistent fusion of frames and events for robust and high-freq point tracking. Repo & Dataset under MIT💙
👉Review https://t.ly/-q4wm
👉Paper https://arxiv.org/pdf/2603.04989
👉Project http://tapformer.github.io/
👉Repo https://github.com/ljx1002/TAPFormer
🍧Monocular 3D Clothed Human🍧
👉MultiGO++ is a novel framework for monocular 3D clothed human reconstruction via geometry-texture collaboration. New SOTA but no code announced🥲
👉Review https://t.ly/YKY44
👉Paper arxiv.org/pdf/2603.04993
👉Project 3dagentworld.github.io/multigo++
Could be useful for you seeing a few (verified) job posting about AI in this channel?
🍙Any Resolution, Any Geometry🍙
👉Ultra Resolution Geometry Transformer (URGT) for arbitrary resolutions (e.g. 4K, 6K, 8K) depth–normal estimation. New SOTA. Repo under MIT💙
👉Review https://t.ly/HXg1n
👉Paper arxiv.org/pdf/2603.03026
👉Project dreamaker-mrc.github.io/Any-Resolution-Any-Geometry/
👉Repo github.com/Dreamaker-MrC/Any-Resolution-Any-Geometry
🐪DuoMo: Dual Motion Diffusion🐪
👉DuoMo by #Meta is a novel generative method that recovers human motion in world-space coordinates from unconstrained videos with noisy or incomplete observations. Code announced💙
👉Review https://t.ly/dnA3K
👉Paper arxiv.org/pdf/2603.03265
👉Project yufu-wang.github.io/duomo/
👉Repo TBA
🪿All Point Clouds-One Encoder🪿
👉Utonia is a step toward one-from-all and one-for-all point cloud encoder. It pretrains a single encoder on diverse point cloud data and reuses it as a reliable backbone for downstream tasks. Code under Apache 2.0💙
👉Review https://t.ly/yqSyZ
👉Paper https://arxiv.org/pdf/2603.03283
👉Project https://pointcept.github.io/Utonia/
👉Repo https://github.com/Pointcept/Utonia
🍓Fully Offline Mobile-VTON🍓
👉A novel, hq, privacy-preserving framework that enables fully offline virtual try-on on commodity mobile devices using only a single user image and a garment image. Repo announced, to be released💙
👉Review https://t.ly/dsrIn
👉Paper arxiv.org/pdf/2603.00947
👉Project zhenchenwan.github.io/Mobile-VTON/
👉Repo https://github.com/tmllab/2026_CVPR_Mobile-VTON
🦜Geometry-Aware 4D Head🦜
👉 GeoDiff4D is a novel framework that reconstructs animatable 4D head avatars from a single portrait image through geometry-aware diffusion. Code announced💙
👉Review https://t.ly/J9L-t
👉Paper https://lnkd.in/ddpv-78g
👉Project https://lnkd.in/d-vhukyj
👉Repo https://lnkd.in/dzd6mnFv
🧱Solaris: generative #Minecraft🧱
👉NYU unveils Solaris, multiplayer video world model in Minecraft, which generates consistent first-person observations for two players simultaneously. Impressive work. Repo & Dataset💙
👉Review https://t.ly/VrcrT
👉Paper https://arxiv.org/pdf/2602.22208
👉Project https://solaris-wm.github.io/
👉Repo https://github.com/solaris-wm/
