AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates on Deep Learning, Machine Learning, and Computer Vision (with Papers).

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/
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🌼SOTA Textured 3D-Guided VTON🌼

πŸ‘‰#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 releasedπŸ’™

πŸ‘‰Review https://t.ly/0tjdC
πŸ‘‰Paper https://lnkd.in/dFseYSXz
πŸ‘‰Project https://lnkd.in/djtqzrzs
πŸ‘‰Repo TBA
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🍏#Nvidia Dynamic Pose 🍏

πŸ‘‰Nvidia unveils DynPose-100K, the largest dataset of dynamic Internet videos annotated with camera poses. Dataset released under Nvidia licenseπŸ’™

πŸ‘‰Review https://t.ly/wrcb0
πŸ‘‰Paper https://lnkd.in/dycGjAyy
πŸ‘‰Project https://lnkd.in/dDZ2Ej_Q
πŸ€—Data https://lnkd.in/d8yUSB7m
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πŸ”₯ S3MOT: SOTA 3D MOT πŸ”₯

πŸ‘‰S3MOT: 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 licenseπŸ’™

πŸ‘‰Review https://t.ly/H_JPv
πŸ‘‰Paper https://arxiv.org/pdf/2504.18068
πŸ‘‰Repo https://github.com/bytepioneerX/s3mot
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πŸ”₯ Diffusion Model <-> Depth πŸ”₯

πŸ‘‰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 availableπŸ’™

πŸ‘‰Review https://t.ly/sP9ma
πŸ‘‰Paper arxiv.org/pdf/2411.19189
πŸ‘‰Project rollingdepth.github.io/
πŸ‘‰Repo github.com/prs-eth/rollingdepth
πŸ€—Demo huggingface.co/spaces/prs-eth/rollingdepthhttps://t.ly/sP9ma
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🩷Dance vs. #ComputerVision🩷

πŸ‘‰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 releasedπŸ’™

πŸ‘‰Review https://t.ly/JEdM3
πŸ‘‰Paper arxiv.org/pdf/2505.07249
πŸ‘‰Project https://lnkd.in/dD5dsMv5
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πŸ§žβ€β™€οΈGENMO: Generalist Human Motion πŸ§žβ€β™€οΈ

πŸ‘‰#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 momentπŸ₯²

πŸ‘‰Review https://t.ly/Q5T_Y
πŸ‘‰Paper https://lnkd.in/ds36BY49
πŸ‘‰Project https://lnkd.in/dAYHhuFU
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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.
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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.
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πŸ¦– DINOv3 is out πŸ¦–

πŸ‘‰#Meta unveils DINOv3! A novel foundation model outperforming the previous SOTAs in computer vision. Code & weights released under DINOv3 LicenseπŸ’™

πŸ‘‰Review https://t.ly/-S3ZL
πŸ‘‰Paper https://t.ly/ervOT
πŸ‘‰Project https://lnkd.in/dHFf3esd
πŸ‘‰Repo https://lnkd.in/dPxhDxAq
πŸ€—HF https://lnkd.in/dWGudY2i
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πŸ€– Impact of SuperHuman AI πŸ€–

πŸ‘‰The NoProfit AI Futures Project unveils a (dystopic) scenario about what super-AI might look like. Forecast from today to the bio-engineered human-like creatures. A fascinating speculation of the future with the "slow-down" and "race" scenarios. Enjoy πŸ’™

πŸ‘‰Review https://t.ly/EgmfJ
πŸ‘‰Project https://ai-2027.com/
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πŸ“TOTNet: Occlusion-aware TrackingπŸ“

πŸ‘‰TOTNet: novel Temporal Occlusion Tracking Network that leverages 3D-convs, visibility-weighted loss, & occlusion augmentation to improve performance under occlusions. Code & Data under MITπŸ’™

πŸ‘‰Review https://t.ly/Q0jAf
πŸ‘‰Paper https://lnkd.in/dUYsa-GC
πŸ‘‰Repo https://lnkd.in/d3QGUHYb
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πŸ”€Feed-Forward 4D videoπŸ”€

πŸ‘‰4DNeX is the first feed-forward framework for generating 4D scene representations from a single image by fine-tuning diffusion model. HQ dynamic pt-clouds & downstream tasks such as novel-view video synthesis with strong generalizability. Code/Data announced πŸ’™

πŸ‘‰Review https://t.ly/SpkD-
πŸ‘‰Paper arxiv.org/pdf/2508.13154
πŸ‘‰Project https://4dnex.github.io/
πŸ‘‰Repo github.com/3DTopia/4DNeX
πŸ‘‰Data https://lnkd.in/dh4_3Ghf
πŸ‘‰Demo https://lnkd.in/dztyzwgg
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🌈DAViD: Synthetic Depth-Normal-Segmentation🌈

πŸ‘‰#Microsoft's DAViD: 100% synthetic dataset/models for human Depth, Normals & Segmentation. Dataset available, models & runtime under MITπŸ’™

πŸ‘‰Review https://t.ly/-SlO_
πŸ‘‰Paper https://lnkd.in/eCmMXpTg
πŸ‘‰Project https://lnkd.in/eurCSWkm
πŸ‘‰Repo https://lnkd.in/e7PWFgP2
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πŸ‘  OmniTry: Virtual Try-On Anything πŸ‘ 

πŸ‘‰OmniTry: unified framework that extends VTON beyond garment to encompass any wearable objects (jewelries, accessories, etc.) in mask-free setting. Weights, HF demo & benchmark releasedπŸ’™

πŸ‘‰Review https://t.ly/wMBGQ
πŸ‘‰Paper https://lnkd.in/dQe9MchS
πŸ‘‰Project https://omnitry.github.io/
πŸ‘‰Repo https://lnkd.in/d3QwAXY2
πŸ€—Demo https://lnkd.in/duUcZpVA
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πŸ“‘ ROVR Open Dataset is out πŸ“‘

πŸ‘‰A novel large-scale open 3D dataset for autonomous driving, robotics, and 4D perception tasks. To be released for academic (for free) & commercialπŸ’™

πŸ‘‰Review https://t.ly/iDcvg
πŸ‘‰Paper https://arxiv.org/pdf/2508.13977
πŸ‘‰Project https://xiandaguo.net/ROVR-Open-Dataset
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πŸ§‰ YOPO: SOTA 9-DoF PoseπŸ§‰

πŸ‘‰Pit In Co. unveils YOPO, a novel single-stage, query-based framework that treats category-level 9-DoF estimation as a natural extension of 2D detection. A practical solution for mono-RGB, category-level, multi-obj pose estimation. Code & models announced (coming)πŸ’™

πŸ‘‰Review https://t.ly/cf_Cl
πŸ‘‰Paper https://arxiv.org/pdf/2508.14965
πŸ‘‰Project mikigom.github.io/YOPO-project-page/
πŸ‘‰Repo TBA
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πŸ”¬Intern-S1: SOTA MM-MoE πŸ”¬

πŸ‘‰InternS1: a MM-MoE with 28B activated / 241b total parameters, continually pre-trained on 5T tokens, including 2.5T+ tokens from scientific domains. New SOTA for professional tasks, such as molecular synthesis planning, reaction condition prediction, etc. Models available under Apache 2.0πŸ’™

πŸ‘‰Review https://t.ly/3l5UW
πŸ‘‰Paper arxiv.org/pdf/2508.15763
πŸ‘‰Repo github.com/InternLM/Intern-S1
πŸ€—HF huggingface.co/internlm/Intern-S1
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πŸ«”ATLAS: SOTA Human ModelπŸ«”

πŸ‘‰#META presents ATLAS, a novel high-fidelity body model learned from 600k high-res. scans captured using 240 synchronized cams. Code announced, to be releasedπŸ’™

πŸ‘‰Review https://t.ly/0hHud
πŸ‘‰Paper arxiv.org/pdf/2508.15767
πŸ‘‰Project jindapark.github.io/projects/atlas/
πŸ‘‰Repo TBA
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🧀Diffusive Hand from Signs🧀

πŸ‘‰LIGM + #NVIDIA unveil a novel generative model of 3D hand motions from Sign Language Data. Motion characteristics such as handshapes, locations, finger, hand & arm movements. Code, Models & Data to be released πŸ’™

πŸ‘‰Review https://t.ly/HonX_
πŸ‘‰Paper https://arxiv.org/pdf/2508.15902
πŸ‘‰Project https://imagine.enpc.fr/~leore.bensabath/HandMDM/
πŸ‘‰Data drive.google.com/drive/u/1/folders/1BLsu2hAqhAJ_gnGb9TNXW7MLiSuSEzEj
πŸ‘‰Repo TBA
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