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
Больше📈 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 17 261 подписчиков, занимая 7 532 место в категории Технологии и приложения и 2 128 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 17 261 подписчиков.
Согласно последним данным от 29 октября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 219, а за последние 24 часа — 0, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 16.46%. В первые 24 часа после публикации контент обычно набирает 7.09% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 842 просмотров. В течение первых суток публикация набирает 1 223 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 17.
- 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 30 октября, 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.
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Архив постов
🦉PANDORA: Polarized Neural Decomposition🦉
👉CIL lab unveils PANDORA: polarimetric inverse rendering approach via INR
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Geometry, reflectance & illumination
✅normal, signed distance field, mesh
✅Diffuse-specular separation
✅Hi-fI incident illumination
More https://bit.ly/3CzGp3F
🍈 #StableDiffusion archive is out🍈
👉Lexica art is a Stable Diffusion prompt search engine. Real-time, countless #stablediffusion results for everyone. I had fun with the GOAT, #Maradona.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Maradona scoring against a capybara...
✅A poster of space jam with Maradona...
✅Painting of Maradona very detailed...
✅Painting of Maradona in heaven...
More: https://bit.ly/3PTXHLH
🌐RelPose: Probabilistic Relative Pose🌐
👉A novel method for core component in #SLAM / NeRF-powered apps.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Core component of SfM/SLAM
✅Pre-processing for neural (NeRF)
✅Energy-based over rotations
✅SOTA on both seen/unseen objects
More: https://bit.ly/3T60TXw
🍈DeepBillboards: old-school trick for #VR🍈
👉DeepBillboards models a 3D object implicitly using neural net on the user’s viewing direction
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅#Google Brain +Tsukuba + Tokyo
✅Rendering at higher res., improving #VR
✅NeRF into interactive VR with accuracy++
✅NeRF (or any others) directly in #Unity
More: https://bit.ly/3CsTQ5y
🥭Massive GTA-V human dataset🥭
👉GTA-Human: outperforming SOTA with a purely synthetic training.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅600+ gender, age, ethnicity & clothing
✅20,000+ clips, variety of human activities
✅6 categories of location, different BGs
✅Occlusions, lighting, and weather system
More: https://bit.ly/3wpZyRD
🔥 KeypointNeRF: code is out! 🔥
👉KeypointNeRF by #Meta: "NeRF"-avatars
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Generalizable NeRF for virtual avatar
✅Sparse 3D keypoints for SOTA avatar
✅Novel unseen subjects from 2/3 views
✅"iPhone" captures for #metaverse
More: https://bit.ly/3pyl17e
🔥🔥UPDATE🔥🔥
Code Released: https://github.com/facebookresearch/KeypointNeRF
🥑 CLIP-based Neural Style Transfer 🥑
👉From #Nvidia a novel method for transferring the style to a #3D object
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Texture style for 3D by CLIP-ResNet50
✅Nearest-neighbor feature matching loss
✅CLIP-based loss extraction of textures
✅NNFM for multiple style pics / control
✅No source code or models available 😒
More: https://bit.ly/3c32dK5
🍏NeuMan: Human NeRF in the wild🍏
👉#Apple opens a novel human pose/view from just a single in-the-wild video
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅No extra devices/annotations
✅Both Human (novel poses) + Scene
✅E2E SMPL optimization + error-corr.
✅Applications such as "telegathering"
More: https://bit.ly/3K4iTO6
🧰 FGT: flow-guided inpainting 🧰
👉#Microsoft (+USTC) unveils FGT: flow-guided ViT for video inpainting 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅OF into transformer for attention++
✅Flow completion net w/ local feats.
✅Dual perspective spatial MHSA
✅Local attention with global content
More: https://bit.ly/3pk5J5S
🐍 Implicitron: "democratizing" NeRF🐍
👉#META opens a novel framework for NeRF-world in #PyTorch3D #pytorch
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Implicit representations (NeRF) / Render
✅RaySampler/PointSampler & more
✅NeRF’s MLP, IDR’s FF, SRN, etc.
✅Renderers: MEAR, LSTMRenderer, etc.
More: https://bit.ly/3bPyJPJ
🔥Stable Diffusion on clips. INSANE🔥
👉The most advanced latent text-to-image DM. #RunwayML just announced is going to apply it on clips
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Latent DM on 512p from LAION-5B
✅Frozen CLIP ViT-L/14 text encoder
✅Lightweight, runs on a 10GB-GPU
✅Checkpoints only for research
More: https://bit.ly/3QfkRx3
🍨 Scaling Neural Indoor Scene 🍨
👉Neural scene rendering for indoor: scalable in both training/rendering
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Neural scene rendering for indoor
✅#3D into tiles with MLPs to scale up
✅Parallel training of tile-based MLPs
✅View-indep. components (via surf-MLP)
More: https://bit.ly/3bH94IX
🎰 Texturify: Neural Textures Generator 🎰
👉A step towards automated content creation. HQ textures directly on surface of 3D object
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅TUM + Max Planck + Apple 🍏
✅Realistic, HQ textures from 2D pics
✅3D shape geometry, no 3D supervision
✅3D-aware surface-based generation net
More: https://bit.ly/3BW7UUU
🪰 EasyMocap: Open Neural Mocap 🪰
👉EasyMocap: open-source marker-less mocap with novel view synthesis from RGB
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 (of last paper added):
✅Editable free-viewpoint video
✅Layered neural representation of humans
✅Multi-pax -> instances, weakly-supervised
✅HQ neural representation of the humans
✅Addressing camera error by human poses
More: https://bit.ly/3p6lUDO
🥇#NVIDIA wins SIGGRAPH's Best Paper🥇
👉Instant #NeRF awarded as a best paper at SIGGRAPH 2022!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Speed-up of several orders of magnitude
✅HQ neural primitives in a matter of secs
✅Render in tens of milliseconds at 1080p
✅Source code and resources available!
More: https://bit.ly/3Qt8c9D
🧊EPro-PnP: Persp-n-Points Detection🧊
👉EPro-PnP: probabilistic PnP layer for general e2e pose estimation
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Probabilistic PnP for general e2e pose
✅Top-tier in 6DoF by inserting into CDPN
✅Deformable accurate detection
✅2D-3D corresp. learned from scratch
More: https://bit.ly/3BNPXYr
🎹🎹 Learning Piano in #AR 🎹🎹
👉PianoVision (on #META #Quest2) accelerates the piano learning via Passthrough #AR & hand tracking
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Sheet Insight to learn sight-read
✅MIDI keyboard connectivity
✅Air piano for no physical pianos
✅Multiplayer Music Instruction
✅PianoVision Music Hall in #VR
More: https://bit.ly/3zYvwGX
🍑 World-Object Detection via ViT 🍑
👉Google unveils OWL-ViT: open-vocabulary detector based on ViTs 🤯
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅ViTs for Open-World Localization
✅Img-level to open-vocabulary detection
✅SOTA one-shot (img.cond.) detection
More: https://bit.ly/3Sy3jOj
