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 17 261 名订阅者，在 技术与应用 类别中位列第 7 491，并在 马来西亚 地区排名第 2 128 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 17 261 名订阅者。
根据 24 十月, 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 258，过去 24 小时变化为 -2，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 16.76%。内容发布后 24 小时内通常能获得 7.11% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 893 次浏览，首日通常累积 1 227 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 16。
- 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 25 十月, 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。
17 261
订阅者
-224 小时
+27 天
+25830 天
帖子存档
🦖 #META's DINOv3 is out 🦖
👉#Meta unveils DINOv3! A novel foundation model outperforming the previous SOTAs in computer vision. Code & weights released under DINOv3 License💙
👉Review https://t.ly/-S3ZL
👉Paper https://t.ly/ervOT
👉Project https://lnkd.in/dHFf3esd
👉Repo https://lnkd.in/dPxhDxAq
🤗HF https://lnkd.in/dWGudY2i
Hi everybody,
I took a few weeks to take a breath from a lot of stuff, I dedicated all my mental energy to keep working and I dedicated all my spare time to take care of myself. Despite I'm still not ok (BTW, my health was/is always good), I feel it's time to come back and support this wonderful community in this journey. I feel the responsibility of that, time to get in the ring.
I'm very sorry for being out so long, but sometime life hits really hard. I got an incredible support from unknown people from all around the world. It's amazing.
Thanks again, you rock!
Alessandro.
Dear friends,
I’m truly sorry for being away from the group for so long. I know: no updates so far while AI is running faster than speed of light.
I’m going through a very difficult time in my life and I need some space to heal. This spare-time project (but important for a lot of people here) needs energy and commitment I don’t have right now. I’m sorry, be patient. I’ll be back.
Love u all,
Alessandro.
🧞♀️GENMO: Generalist Human Motion 🧞♀️
👉#Nvidia presents GENMO, a unified Generalist Model for Human Motion that bridges motion estimation and generation in a single framework. Conditioning on videos, 2D keypoints, text, music, and 3D keyframes. No code at the moment🥲
👉Review https://t.ly/Q5T_Y
👉Paper https://lnkd.in/ds36BY49
👉Project https://lnkd.in/dAYHhuFU
🩷Dance vs. #ComputerVision🩷
👉The Saint-Etienne university proposed a new 3D human body pose estimation pipeline to deal with dance analysis. Project page w/ results and interactive demo released💙
👉Review https://t.ly/JEdM3
👉Paper arxiv.org/pdf/2505.07249
👉Project https://lnkd.in/dD5dsMv5
🔥 Diffusion Model <-> Depth 🔥
👉ETH & CMU on how to turn a single-image latent diffusion model (LDM) into the SOTA video depth estimator: video depth without video models. Repo released under Apache 2.0 and HF demo available💙
👉Review https://t.ly/sP9ma
👉Paper arxiv.org/pdf/2411.19189
👉Project rollingdepth.github.io/
👉Repo github.com/prs-eth/rollingdepth
🤗Demo huggingface.co/spaces/prs-eth/rollingdepthhttps://t.ly/sP9ma
🔥 S3MOT: SOTA 3D MOT 🔥
👉Wuhan University unveils S3MOT, a Selective-State-Space model-based MOT that efficiently infers 3D motion and object associations from 2D images through three core components. New SOTA on KITTI with 76.86 HOTA at 31 FPS! Code & Weights to be released under MIT license💙
👉Review https://t.ly/H_JPv
👉Paper https://arxiv.org/pdf/2504.18068
👉Repo https://github.com/bytepioneerX/s3mot
🍏#Nvidia Dynamic Pose 🍏
👉Nvidia unveils DynPose-100K, the largest dataset of dynamic Internet videos annotated with camera poses. Dataset released under Nvidia license💙
👉Review https://t.ly/wrcb0
👉Paper https://lnkd.in/dycGjAyy
👉Project https://lnkd.in/dDZ2Ej_Q
🤗Data https://lnkd.in/d8yUSB7m
🌼SOTA Textured 3D-Guided VTON🌼
👉#ALIBABA unveils 3DV-TON, a novel diffusion model for HQ and temporally consistent video. Generating animatable textured 3D meshes as explicit frame-level guidance, alleviating the issue of models over-focusing on appearance fidelity at the expanse of motion coherence. Code & benchmark to be released💙
👉Review https://t.ly/0tjdC
👉Paper https://lnkd.in/dFseYSXz
👉Project https://lnkd.in/djtqzrzs
👉Repo TBA
📍Moving Points -> Depth📍
👉KAIST & Adobe propose Seurat, a novel method that infers relative depth by examining the spatial relationships and temporal evolution of a set of tracked 2D trajectories (via off-the-shelf point tracking models). Repo & Demo to be released💙
👉Review https://t.ly/qA2P5
👉Paper https://lnkd.in/dpXDaQtM
👉Project https://lnkd.in/d9qWYsjP
👉Repo https://lnkd.in/dZEMDiJh
🦧 #Nvidia Describe Anything 🦧
👉Nvidia unveils Describe Anything Model (DAM) the new SOTA in generating detailed descriptions for user-specified regions in images/videos, marked by points, boxes, scribbles, or masks. Repo under Apache, Dataset available and live demo on 🤗
👉Review https://t.ly/la4JD
👉Paper https://lnkd.in/dZh82xtV
👉Project https://lnkd.in/dcv9V2ZF
👉Repo https://lnkd.in/dJB9Ehtb
🤗Demo https://lnkd.in/dXDb2MWU
🧊TAP in Persistent 3D Geometry🧊
👉TAPIP3D is the novel SOTA for long-term 3D point tracking in mono-RGB/RGB-D. Videos as camera-stabilized spatio-temporal feature clouds, leveraging depth & motion to lift 2D video feats into a 3D world space where camera motion is effectively canceled. Code under Apache💙
👉Review https://t.ly/oooMy
👉Paper https://lnkd.in/d8uqjdE4
👉Project https://tapip3d.github.io/
👉Repo https://lnkd.in/dsvHP_8u
🔥 #Apple Co-Motion is out! 🔥
👉Apple unveils a novel approach for detecting & tracking detailed 3D poses of multiple people from single monocular stream. Temporally coherent predictions in crowded scenes with hard poses & occlusions. New SOTA, 10x faster! Code & Models released only for research💙
👉Review https://t.ly/-86CO
👉Paper https://lnkd.in/dQsVGY7q
👉Repo https://lnkd.in/dh7j7N89
🔍Event Blurry Super-Resolution🔍
👉USTC unveils Ev-DeblurVSR: event signals into BVSR for a novel event-enhanced network. Blurry Video Super-Resolution (BVSR) aiming at generating HR videos from low-resolution and blurry inputs. Pretrained models and test released under Apache💙
👉Review https://t.ly/x6hRs
👉Paper https://lnkd.in/dzbkCJMh
👉Repo https://lnkd.in/dmvsc-yS
🔥General attention-based object🔥
👉GATE3D is a novel framework designed specifically for generalized monocular 3D object detection via weak supervision. GATE3D effectively bridges domain gaps by employing consistency losses between 2D and 3D predictions.
👉Review https://t.ly/O7wqH
👉Paper https://lnkd.in/dc5VTUj9
👉Project https://lnkd.in/dzrt-qQV
🐯UniAnimate-DiT: Human Animation🐯
👉UniAnimate-DiT is a novel n' effective framework based on Wan2.1 for consistent human image animation. LoRAs to finetune the model parameters -reducing memory- maintaining the original model’s generative skills. Training and inference code released💙
👉Review https://t.ly/1I50N
👉Paper https://arxiv.org/pdf/2504.11289
👉Repo https://github.com/ali-vilab/UniAnimate-DiT
🍏PartField #3D Part Segmentation🍏
👉#Nvidia unveils PartField, a FFW approach for learning part-based 3D features, which captures the general concept of parts and their hierarchy. Suitable for single-shape decomposition, co-segm., correspondence & more. Code & Models released under Nvidia License💙
👉Review https://t.ly/fGb2O
👉Paper https://lnkd.in/dGeyKSzG
👉Code https://lnkd.in/dbe57XGH
👉Project https://lnkd.in/dhEgf7X2
🍄 4D Mocap Human-Object 🍄
👉#Adobe unveils HUMOTO, HQ dataset of human-object interactions for motion generation, computer vision, and robotics: 700+ sequences (7,875 seconds @ 30FPS), interactions with 63 precisely modeled objects and 72 articulated parts
👉Review https://t.ly/lCof3
👉Paper https://lnkd.in/dVVBDd_c
👉Project https://lnkd.in/dwBcseDf
