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
Ko'proq ko'rsatish📈 Telegram kanali AI with Papers - Artificial Intelligence & Deep Learning analitikasi
AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 17 262 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 491-o'rinni va Malayziya mintaqasida 2 128-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 262 obunachiga ega bo‘ldi.
23 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 255 ga, so‘nggi 24 soatda esa 2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 18.93% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 7.17% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 267 marta ko‘riladi; birinchi sutkada odatda 1 237 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 20 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent framework, object, dataset, tba, depth kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 24 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
17 262
Obunachilar
+224 soatlar
+67 kun
+25530 kun
Postlar arxiv
🔥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
🔥 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/
🌍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
🦙 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
⭐ 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💙
🎯Generative Refocusing is out🎯
👉Generative Refocusing is a two-step process that uses DeblurNet to recover all-in-focus images from various inputs and BokehNet for creating controllable bokeh (in semi-supervised mode). Repo under Apache2.0💙
👉Review https://t.ly/8t7PA
👉Paper arxiv.org/pdf/2512.16923
👉Project generative-refocusing.github.io/
👉Repo github.com/rayray9999/Genfocus
👉Demo huggingface.co/spaces/nycu-cplab/Genfocus-Demo
🏜️ Depth Any Panoramas 🏜️
👉DAP is the new SOTA foundation model for panoramic depth estimation with a large scale dataset. Data & Repo under MIT💙
👉Review https://t.ly/LaUmd
👉Paper arxiv.org/pdf/2512.16913
👉Project https://lnkd.in/dvqNV9jx
👉Repo https://lnkd.in/dmNzhb-7
👉Demo https://lnkd.in/dDwjMF3u
🫠 FlexAvatar: 3D Heads Avatars 🫠
👉TUM introduces FlexAvatar, a novel method for creating HQ and complete 3D head avatars from a single image. Code announced💙
👉Review https://t.ly/Rkdtd
👉Paper arxiv.org/pdf/2512.15599
👉Project tobias-kirschstein.github.io/flexavatar/
👉Repo TBA
👀DriverGaze360: Driver SOTA👀
👉DriverGaze360 is a large-scale 360◦ field of view driver attention dataset, containing ∼1M gaze-labeled frames. Code & Dataset announced💙
👉Review https://t.ly/ZcoUw
👉Paper arxiv.org/pdf/2512.14266
👉Project av.dfki.de/drivergaze360/
👉Repo github.com/dfki-av/drivergaze360
👉Data av.dfki.de/drivergaze360/dataset
💷 SOTA Zero-Shot Stereo Matching💷
👉Fast-FoundationStereo by #Nvidia is a novel family of architectures that achieve, for the first time, strong zero-shot generalization at real-time frame rate via divide-&-conquer acceleration. Code & Data announced💙
👉Review https://t.ly/XD6pO
👉Paper https://lnkd.in/d9_YKW2A
👉Project https://lnkd.in/dKDxm7EX
👉Repo https://lnkd.in/dR4-PdsW
💚 MatAnyone 2 is out! 💚
👉MatAnyone 2 is the most advanced human video matting framework that preserves fine details by avoiding segmentation-like boundaries, while also shows enhanced robustness under challenging real-world conditions. Repo & Dataset announced💙
👉Review https://www.linkedin.com/posts/visionarynet_artificialintelligence-ai-deeplearning-activity-7406244283055525888-8Dl_
👉Paper arxiv.org/pdf/2512.11782
👉Project pq-yang.github.io/projects/MatAnyone2
👉Repo github.com/pq-yang/MatAnyone2
🫎 MoCapAnything is out 🫎
👉MoCapAnything is novel a reference-guided, factorized framework that first predicts 3D joint trajectories and then recovers asset-specific rotations via constraint-aware IK fitting. No code announced 🥲
👉Review https://t.ly/_Tw6t
👉Paper arxiv.org/pdf/2512.10881
👉Project animotionlab.github.io/MoCapAnything
🧱 Pixel Art Volumetric Rendering 🧱
👉Voxify3D is a novel differentiable two-stage framework bridging 3D mesh optimization with 2D pixel art supervision. Repo announced💙
👉Review https://t.ly/qPyNl
👉Paper https://lnkd.in/du5ikJGN
👉Project https://lnkd.in/dpiAjj5m
👉Repo TBA
🎷Layered PSD Diffusion🎷
👉OmniPSD produces layered PSD files with transparent alpha channels, separating text, foreground elements, and background into clean RGBA layers that can be directly edited in tools. Online Demo💙
👉Review https://t.ly/YNRAC
👉Paper arxiv.org/pdf/2512.09247
👉Project showlab.github.io/OmniPSD/
👉Demo https://www.lovart.ai/it
🐘TTSC for 3D Generative🐘
👉SpaceControl is the new SOTA training-free test-time method for explicit spatial control of 3D generation. Repo announced💙
👉Review https://t.ly/1zrah
👉Paper https://lnkd.in/dEWh3vep
👉Project https://lnkd.in/dScftUmm
👉Repo TBA
✌️SOTA Generative SLP✌️
👉Stable Signer is a new sign language generative model. It redefines the SLP task as a hierarchical generation end-to-end task that only includes text understanding (Prompt2Gloss, Text2Gloss) and Pose2Vid. Repo with data 💙
👉Review https://t.ly/yKZhn
👉Paper arxiv.org/pdf/2512.04048
👉Project stablesigner.github.io/
👉Data github.com/SignLLM/Prompt2Sign/tree/main/tools-new-2025
🦄 Native Unified Multimodal 🦄
👉META unveils a novel UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows E2E processing of images/videos for both understanding/generation. Code under legal review💙
👉Review https://t.ly/7wmKP
👉Paper https://lnkd.in/djT4WGEU
👉Project https://tuna-ai.org/
👉Repo github.com/wren93/tuna
🥭3D Point Motion Editing🥭
👉Edit-by-Track enables precise video motion editing via 3D point tracks. By specifying desired 3D trajectories, users can seamlessly control joint camera and object motion, remove objects, and transfer motion between videos. No code announced but it's a relevant paper💙
👉Review https://t.ly/lqzHY
👉Paper https://arxiv.org/pdf/2512.02015
👉Project https://edit-by-track.github.io/
