AI with Papers - Artificial Intelligence & Deep Learning
前往频道在 Telegram
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
显示更多📈 Telegram 频道 AI with Papers - Artificial Intelligence & Deep Learning 的分析概览
频道 AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 17 036 名订阅者，在 技术与应用 类别中位列第 7 452，并在 马来西亚 地区排名第 2 166 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 17 036 名订阅者。
根据 02 九月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -1，过去 24 小时变化为 5，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 12.68%。内容发布后 24 小时内通常能获得 7.60% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 159 次浏览，首日通常累积 1 295 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 13。
- 主题关注点： 内容集中在 framework, object, dataset, tba, depth 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“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”
凭借高频更新（最新数据采集于 03 九月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
17 036
订阅者
+524 小时
+127 天
-130 天
帖子存档
🛒 Reshoot-Anything is out 🛒
👉Reshoot-Anything reshoots dynamic monocular videos under novel camera trajectories. Code under Apache 2.0 💙
👉Review https://t.ly/MIqAc
👉Paper https://arxiv.org/pdf/2604.21776
👉Project adithyaiyer1999.github.io/reshoot-anything/
👉Repo github.com/morphicfilms/video-to-video
💙 PY4AI 2026: here we are! 💙
👉The third edition of our conference is official! Speaker list and (free) tickets: https://t.ly/L4_52
🎈Face Anything 4D (SOTA)🎈
👉A novel unified 4D facial reconstruction and dense tracking from image sequences: new SOTA in facial single-image and mono-video depth estimation, dense 4D reconstruction, and 3D point tracking. Repo & Dataset announced💙
👉Review https://t.ly/zItie
👉Paper https://arxiv.org/pdf/2604.19702
👉Project kocasariumut.github.io/FaceAnything
👉Repo TBA
🌗Mobile Ultra-detailed Avatars🌗
👉Given skeletal poses and a virtual camera as inputs, MUA by Max Planck Institute produces photorealistic renderings and hyper-detailed geometry of animatable clothed humans. Repo announced💙
👉Review https://t.ly/QPCy6
👉Paper https://arxiv.org/pdf/2604.18583
👉Project https://vcai.mpi-inf.mpg.de/projects/MUA/
👉Repo TBA
👩🦰 3D Head w/ Deformable Hair 👩🦰
👉Xi’an Jiaotong University unveils a novel method that reconstructs decoupled 3D Gaussian head avatars from a single input image: effortless hairstyle transfer with natural dynamic hair motion. Code announced💙
👉Review https://t.ly/kWZdd
👉Paper https://arxiv.org/pdf/2604.14782
👉Project yuansun-xjtu.github.io/CompHairHead.io/
👉Repo yuansun-xjtu.github.io/CompHairHead.io/
🐞GCT 3D Reconstruction🐞
👉ANT unveils LingBot-Map, a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. Repo under A-NC 4.0 International💙
👉Review https://t.ly/ExodA
👉Paper https://arxiv.org/pdf/2604.14141
👉Project https://arxiv.org/pdf/2604.14141
👉Repo github.com/robbyant/lingbot-map
📱3D Human-Object Contact📱
👉Pi-HOC by CMU + NREC is a novel single-pass, instance-aware framework for dense 3D semantic contact prediction of all human-object pairs. Repo announced💙
👉Review https://t.ly/TAgG1
👉Paper https://arxiv.org/pdf/2604.12923
👉Project https://pi-hoc.github.io/
👉Repo https://github.com/SravanChittupalli/Pi-HOC
🐓Interactive Objects from EgoVideo🐓
👉EgoFun3D by Simon Fraser University is a coordinated task, dataset and benchmark for modeling interactive 3D objects from egocentric videos. Repo (TBA), demo & dataset💙
👉Review https://t.ly/YhGN7
👉Paper arxiv.org/pdf/2604.11038
👉Project 3dlg-hcvc.github.io/EgoFun3D/
👉Repo github.com/3dlg-hcvc/EgoFun3D
👉Demo bc79fea884062374b3.gradio.live/
🧴OmniShow: Automatic Contents Creation🧴
👉OmniShow is the novel SOTA in content creation with industry-grade performance. Impressive results, best with audio. Repo announced💙
👉Review https://t.ly/Pm-7U
👉Paper arxiv.org/pdf/2604.11804
👉Project correr-zhou.github.io/OmniShow/
👉Repo github.com/Correr-Zhou/OmniShow
🔥SOTA 3D Detection in the wild🔥
👉WildDet3D is a novel unified geometry-aware architecture that natively accepts text, point, and box prompts and can incorporate auxiliary depth signals at inference time. New SOTA! Repo, models & #iphone💙
👉Review https://t.ly/8NxBN
👉Paper https://arxiv.org/pdf/2604.08626
👉Project https://allenai.github.io/WildDet3D/
👉Repo https://github.com/allenai/WildDet3D
🐞6D Object Pose w/ Deformation🐞
👉DeSOPE by Xidian & #MagicLeap is a novel large-scale dataset for 6DoF deformed objects: 665K pose annotations produced via a semiautomatic pipeline. Repo & Dataset announced💙
👉Review https://t.ly/M5VgX
👉Paper https://arxiv.org/pdf/2604.06720
👉Project https://desope-6d.github.io/
👉Repo TBA
🪞1.1M Metric VTON Dataset🪞
👉Google's Fit-Inclusive Try-on: large-scale VTO dataset comprising over 1.13M try-on image triplets accompanied by precise body and garment measurements. Repo & dataset announced💙
👉Review https://t.ly/cs-pt
👉Paper arxiv.org/pdf/2604.08526
👉Project johannakarras.github.io/FIT/
👉Repo TBA
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
