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

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Малайзия2 240 Технологии и приложения7 726...

📈 Аналитический обзор Telegram-канала AI with Papers - Artificial Intelligence & Deep Learning

Канал AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 17 154 подписчиков, занимая 7 726 место в категории Технологии и приложения и 2 240 место в регионе Малайзия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 154 подписчиков.

Согласно последним данным от 21 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -166, а за последние 24 часа — -6, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 23.63%. В первые 24 часа после публикации контент обычно набирает 6.86% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 4 057 просмотров. В течение первых суток публикация набирает 1 177 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 26.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как framework, object, dataset, tba, depth.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”

Благодаря высокой частоте обновлений (последние данные получены 22 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

17 154

Подписчики

-624 часа

-277 дней

-16630 день

4 057

Просмотры поста

~ 1 17724 часа

~ 1 35348 часов

23.63%

Коэффициент вовлеченности

Нет данных

Постов в день

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Архив постов

17 154

🥎POKEFLEX: Soft Object Dataset🥎 👉PokeFlex from ETH is a dataset that includes 3D textured meshes, point clouds, RGB & depth maps of deformable objects. Pretrained models & dataset announced💙 👉Review https://t.ly/GXggP 👉Paper arxiv.org/pdf/2410.07688 👉Project https://lnkd.in/duv-jS7a 👉Repo

17 154

💡Diffusion Models Relighting💡 👉#Netflix unveils DifFRelight, a novel free-viewpoint facial relighting via diffusion model. Precise lighting control, high-fidelity relit facial images from flat-lit inputs. 👉Review https://t.ly/fliXU 👉Paper arxiv.org/pdf/2410.08188 👉Project www.eyelinestudios.com/research/diffrelight.html

17 154

🥦Gaussian Splatting VTON🥦 👉GS-VTON is a novel image-prompted 3D-VTON which, by leveraging 3DGS as the 3D representation, enables the transfer of pre-trained knowledge from 2D VTON models to 3D while improving cross-view consistency. Code announced💙 👉Review https://t.ly/sTPbW 👉Paper arxiv.org/pdf/2410.05259 👉Project yukangcao.github.io/GS-VTON/ 👉Repo github.com/yukangcao/GS-VTON

17 154

🐏 EFM3D: 3D Ego-Foundation 🐏 👉#META presents EFM3D, the first benchmark for 3D object detection and surface regression on HQ annotated egocentric data of Project Aria. Datasets & Code released💙 👉Review https://t.ly/cDJv6 👉Paper arxiv.org/pdf/2406.10224 👉Project www.projectaria.com/datasets/aeo/ 👉Repo github.com/facebookresearch/efm3d

17 154

🔥 "Deep Gen-AI" Full Course 🔥 👉A fresh course from Stanford about the probabilistic foundations and algorithms for deep generative models. A novel overview about the evolution of the genAI in #computervision, language and more... 👉Review https://t.ly/ylBxq 👉Course https://lnkd.in/dMKH9gNe 👉Lectures https://lnkd.in/d_uwDvT6

17 154

🛳️ EVER Ellipsoid Rendering 🛳️ 👉UCSD & Google present EVER, a novel method for real-time differentiable emission-only volume rendering. Unlike 3DGS it does not suffer from popping artifacts and view dependent density, achieving ∼30 FPS at 720p on #NVIDIA RTX4090. 👉Review https://t.ly/zAfGU 👉Paper arxiv.org/pdf/2410.01804 👉Project half-potato.gitlab.io/posts/ever/

17 154

🦴 One-Image Object Detection 🦴 👉Delft University (+Hensoldt Optronics) introduces OSSA, a novel unsupervised domain adaptation method for object detection that utilizes a single, unlabeled target image to approximate the target domain style. Code released💙 👉Review https://t.ly/-li2G 👉Paper arxiv.org/pdf/2410.00900 👉Code github.com/RobinGerster7/OSSA

17 154

🍇SPARK: Real-time Face Capture🍇 👉Technicolor Group unveils SPARK, a novel high-precision 3D face capture via collection of unconstrained videos of a subject as prior information. New SOTA able to handle unseen pose, expression and lighting. Impressive results. Code & Model announced💙 👉Review https://t.ly/rZOgp 👉Paper arxiv.org/pdf/2409.07984 👉Project kelianb.github.io/SPARK/ 👉Repo github.com/KelianB/SPARK/

17 154

👩‍🦰 SOTA Gaussian Haircut 👩‍🦰 👉ETH et. al unveils Gaussian Haircut, the new SOTA in hair reconstruction via dual representation (classic + 3D Gaussian). Code and Model announced💙 👉Review https://t.ly/aiOjq 👉Paper arxiv.org/pdf/2409.14778 👉Project https://lnkd.in/dFRm2ycb 👉Repo https://lnkd.in/d5NWNkb5

17 154

🌾 New SOTA Edge Detection 🌾 👉CUP (+ ESPOCH) unveils the new SOTA for Edge Detection (NBED); superior performance consistently across multiple benchmarks, even compared with huge computational cost and complex training models. Source Code released💙 👉Review https://t.ly/zUMcS 👉Paper arxiv.org/pdf/2409.14976 👉Code github.com/Li-yachuan/NBED

17 154

🩰 Dressed Humans in the wild 🩰 👉ETH (+ #Microsoft ) ReLoo: novel 3D-HQ reconstruction of humans dressed in loose garments from mono in-the-wild clips. No prior assumptions about the garments. Source Code announced, coming 💙 👉Review https://t.ly/evgmN 👉Paper arxiv.org/pdf/2409.15269 👉Project moygcc.github.io/ReLoo/ 👉Code github.com/eth-ait/ReLoo

17 154

🎢 Robo-quadruped Parkour🎢 👉LAAS-CNRS unveils a novel RL approach to perform agile skills that are reminiscent of parkour, such as walking, climbing high steps, leaping over gaps, and crawling under obstacles. Data and Code available💙 👉Review https://t.ly/-6VRm 👉Paper arxiv.org/pdf/2409.13678 👉Project gepetto.github.io/SoloParkour/ 👉Code github.com/Gepetto/SoloParkour

17 154

🌏 JoyHallo: Mandarin Digital Human 🌏 👉JD Health faced the challenges of audio-driven video generation in Mandarin, a task complicated by the language’s intricate lip movements and the scarcity of HQ datasets. Impressive results (-> audio ON). Code Models available💙 👉Review https://t.ly/5NGDh 👉Paper arxiv.org/pdf/2409.13268 👉Project jdh-algo.github.io/JoyHallo/ 👉Code github.com/jdh-algo/JoyHallo

17 154

🌏 JoyHallo: Mandarin Digital Human 🌏 👉JD Health faced the challenges of audio-driven video generation in Mandarin, a task complicated by the language’s intricate lip movements and the scarcity of HQ datasets. Impressive results (-> audio ON). Code Models available💙 👉Review 👉Paper arxiv.org/pdf/2409.13268 👉Project jdh-algo.github.io/JoyHallo/ 👉Code github.com/jdh-algo/JoyHallo

17 154

⚽ SoccerNet 2024 Results ⚽ 👉SoccerNet is the annual video understanding challenge for football. These challenges aim to advance research across multiple themes in football. The 2024 results are out! 👉Review https://t.ly/DUPgx 👉Paper arxiv.org/pdf/2409.10587 👉Repo github.com/SoccerNet 👉Project www.soccer-net.org/

17 154

🧸Motion Instruction Fine-Tuning🧸 👉MotIF is a novel method that fine-tunes pre-trained VLMs to equip the capability to distinguish nuanced robotic motions with different shapes and semantic groundings. A work by MIT, Stanford, and CMU. Source Code announced, coming💙 👉Review https://t.ly/iJ2UY 👉Paper https://arxiv.org/pdf/2409.10683 👉Project https://motif-1k.github.io/ 👉Code coming

17 154

🌭Hand-Object interaction Pretraining🌭 👉Berkeley unveils HOP, a novel approach to learn general robot manipulation priors from 3D hand-object interaction trajectories. 👉Review https://t.ly/FLqvJ 👉Paper https://arxiv.org/pdf/2409.08273 👉Project https://hgaurav2k.github.io/hop/

17 154

💄Interactive Drag-based Editing💄 👉CSE unveils InstantDrag: novel pipeline designed to enhance editing interactivity and speed, taking only an image and a drag instruction as input. Source Code announced, coming💙 👉Review https://t.ly/hy6SL 👉Paper arxiv.org/pdf/2409.08857 👉Project joonghyuk.com/instantdrag-web/ 👉Code github.com/alex4727/InstantDrag

17 154

🫒 Omni Urban Scene Reconstruction 🫒 👉OmniRe is novel holistic approach for efficiently reconstructing HD dynamic urban scenes from on-device logs. It's able to create the simulation of reconstructed scenarios with actors in real-time (~60 Hz). Code released💙 👉Review https://t.ly/SXVPa 👉Paper arxiv.org/pdf/2408.16760 👉Project ziyc.github.io/omnire/ 👉Code github.com/ziyc/drivestudio

17 154

🐺 Diffusion Game Engine 🐺 👉#Google unveils GameNGen: the first game engine powered entirely by a neural #AI that enables real-time interaction with a complex environment over long trajectories at HQ. No code announced but I love it 💙 👉Review https://t.ly/_WR5z 👉Paper https://lnkd.in/dZqgiqb9 👉Project https://lnkd.in/dJUd2Fr6