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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 17 173 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 7 725-o'rinni va Malayziya mintaqasida 2 238-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 17 173 obunachiga ega bo‘ldi.
19 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -177 ga, so‘nggi 24 soatda esa -9 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 21.83% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 749 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 26 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 20 Iyun, 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.
17 173
Obunachilar
-924 soatlar
-397 kunlar
-17730 kunlar
Postlar arxiv
Here the preview, tomorrow the full clip from official source :)
Hinton our guest in Pavia (remotely) 💚😈
🔥BoxerNet: SOTA 2D->3D BBs🔥
👉Boxer by #META: transformer-based network to lift 2D BB proposals into 3D, followed by multi-view fusion and geometric filtering to produce globally consistent de-duplicated 3DBBs in metric world space. Repo under A-NC 4.0 International💙
👉Review https://t.ly/mlmV1
👉Paper https://arxiv.org/pdf/2604.05212
👉Project facebookresearch.github.io/boxer/
👉Repo github.com/facebookresearch/boxer
🔥Vanast: VTON w/ Human Animation🔥
👉SNU unveils a novel unified framework that generates garment-transferred human animation videos directly from a single human/garment images, and pose guidance clip. Repo announced💙
👉Review https://t.ly/c0t79
👉Paper arxiv.org/pdf/2604.04934
👉Project hyunsoocha.github.io/vanast/
👉Repo github.com/snuvclab/vanast
🍎Video Object Deletion🍎
👉Void by Netflix is a novel video object removal framework designed to perform physically-plausible inpainting in very complex scenarios. Repo under Apache 2.0💙
👉Review https://t.ly/cMVny
👉Paper https://arxiv.org/pdf/2604.02296
👉Project https://void-model.github.io/
👉Repo https://github.com/Netflix/void-model
If you have to invest TODAY 1B$ on a frontier tech for the next decade, would you invest in space, agentic, quantum or frugal GPUs? Vote here: https://t.ly/hSx6i
🪬Camera Raw Image Generation🪬
👉RawGen by #Samsung is a generative approach that learns the complex distribution of raw sensor data directly, enabling high-fidelity generation from either text descriptions or standard sRGB images across arbitrary camera sensors. Linear raw image once, then apply any ISP operation. Repo announced💙
👉Review https://t.ly/_QVKP
👉Paper https://arxiv.org/pdf/2604.00093
👉Project https://dy112.github.io/rawgen-page/
👉Repo TBA
🌵SOTA Training-Free In-Context Segmentation🌵
👉INSID3 is the new SOTA, training-free approach that segments concepts at varying granularities only from frozen DINOv3 features, given an in-context example. Repo under Apache 2.0💙
👉Review https://t.ly/NVWHN
👉Paper https://arxiv.org/pdf/2603.28480
👉Project https://visinf.github.io/INSID3/
👉Repo https://github.com/visinf/INSID3
👌HandX: Scaling Hands Motion👌
👉 HandX is a unified foundation spanning data, annotation, and evaluation: novel large-scale dataset of bimanual & dexterous motions with fine-grained textual. Around 6M frames. Repo available💙
👉Review https://t.ly/1nGxw
👉Paper https://arxiv.org/pdf/2603.28766
👉Project https://handx-project.github.io/
👉Repo github.com/handx-project/HandX
💥 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
