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
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šŸ•·ļøHuman Universal GraspingšŸ•·ļø

šŸ‘‰HUG is a flow-matching model that generates diverse human grasps for any user-specified object in a single RGB-D image captured from a stereo camera.

šŸ‘‰Review https://t.ly/VG1Eu
šŸ‘‰Paper https://arxiv.org/pdf/2606.17054
šŸ‘‰Repo https://github.com/KevinyWu/hug
šŸ‘‰Project https://grasping.io/
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šŸ”ŠVolHuMe - Volumetric Human MeshesšŸ”Š

šŸ‘‰VolHuMe (H/T @Martinella_94) is a novel, high-resolution large-scale dataset of volumetric human meshes with complete 4D GT: multi-view RGB-D, textured meshes, dense point clouds, normal maps, rigged assets, garment segmentation, and SMPL-X fittings in one dataset. InsanešŸ’™

šŸ‘‰Review https://t.ly/b5vxy
šŸ‘‰Paper https://arxiv.org/pdf/2606.23062
šŸ‘‰Project giuli13.github.io/volhume-website/#
šŸ‘‰Repo TBA soon
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šŸ‘‹ Hi everyone!

Over the past few weeks, the number of join requests has increased dramatically, which unfortunately also means a much higher number of spam and bots (in the last days around five hundreds been cut off)

To help me distinguish real people from fake profiles - and avoid rejecting genuine requests by mistake - I'd really appreciate if your profile includes:
šŸ“· A real profile photo
šŸ‘¤ Your full name (or something reasonably identifiable)
šŸ’¬ If you contact me, please use English if possible.

I don't speak Russian, Arabic, or Chinese, so if your profile and messages are only in those languages, it's very difficult for me to tell whether you're a real person or an automated account. Thank you for your understanding and for helping keep this damn community welcoming and spam-free!

With love,
Alessandro 😈
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šŸ€OctoSense: Open SensingšŸ€

šŸ‘‰OctoSense is an open-source sensor platform with stereo RGB and event cameras, LiDAR, a thermal camera, an inertial measurement unit, RTK-corrected global positioning system, and proprioception.

šŸ‘‰Review https://t.ly/oFN8L
šŸ‘‰Paper https://lnkd.in/dM3zpyju
šŸ‘‰Project https://lnkd.in/ddrQ3uJ6
šŸ‘‰Repo https://lnkd.in/dhSDjSfG
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šŸ›øPriorEye: Geospatial Self-DrivingšŸ›ø

šŸ‘‰MRG (Oxford) introduces geospatial visual priors to leverage the street-level images in autonomous driving. Consistent improvement in performance. Repo under ApachešŸ’™

šŸ‘‰Review https://t.ly/7Jgav
šŸ‘‰Paper https://lnkd.in/dYeD2m7n
šŸ‘‰Project https://lnkd.in/dWJvNemr
šŸ‘‰Repo https://lnkd.in/dNExGGtx
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šŸŒ’LUNA: Universal 3D AnimationšŸŒ”

šŸ‘‰LUNA by HKUST + META is a novel LBS-free universal neural animation model that directly maps multiple 2D controls like images, keypoints, sketch and unseen characters into 3D-G deformations, bypassing explicit body fitting.

šŸ‘‰Review https://t.ly/ZX9Ex
šŸ‘‰Paper https://arxiv.org/pdf/2606.31981
šŸ‘‰Project https://penghtyx.github.io/LUNA/
šŸ‘‰Repo N/A 🄲
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šŸ”„Nvidia SpatialClaw is outšŸ”„

šŸ‘‰From Nvidia a novel training-free framework for spatial reasoning that adopts code as the action interface. SpatialClaw lets a VLM-backed agent write Python in a persistent kernel, composing perception modules, inspecting intermediate results, and revising its strategy across steps. Impressive: +11.2 points on 20 benchmarksšŸ’™

šŸ‘‰Review https://t.ly/7JB0x
šŸ‘‰Paper https://arxiv.org/pdf/2606.13673
šŸ‘‰Project https://spatialclaw.github.io/
šŸ‘‰Repo https://github.com/NVlabs/SpatialClaw
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šŸÆWorldwide Semantic FacadešŸÆ

šŸ‘‰A centimeter-accurate / cross-continental facade point clouds, with fine-grained semantic segmentation of architectural elements, and hierarchical facade taxonomy. 2.7B DatasetšŸ’™

šŸ‘‰Review https://t.ly/PpyFD
šŸ‘‰Paper https://arxiv.org/pdf/2607.02018
šŸ‘‰Project jiangyuanwangyi.github.io/UnderOneFacade_official
šŸ‘‰Data drive.google.com/drive/folders/1Yzz7PmyeK1qeOtkTFCfkbw7IEHXcMJo8
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šŸˆā€ā¬›Spatial-perception native ViTšŸˆā€ā¬›

šŸ‘‰LingBot-Vision, a vision foundation model pretrained to be spatial-perception native. Better than 7x bigger foundational models. Repo under ApachešŸ’™

šŸ‘‰Review https://t.ly/9xIso
šŸ‘‰Paper https://arxiv.org/pdf/2607.05247
šŸ‘‰Project https://technology.robbyant.com/lingbot-vision
šŸ‘‰Repo https://github.com/robbyant/lingbot-vision
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šŸµļøSoccerNet 2026 ResultsšŸµļø

šŸ‘‰The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video understandingšŸ’™

šŸ‘‰Review https://t.ly/sfD4T
šŸ‘‰Paper https://lnkd.in/dSBgW_3s
šŸ‘‰Project https://lnkd.in/dfdmuvG8
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šŸ”„ZipDepth: Depth on Any DevicešŸ”„

šŸ‘‰ZipDepth from UniBO is a super-compact monocular depth network by combining an efficient reparameterizable encoder-decoder with large-scale knowledge distillation from a foundation model. Repo under MITšŸ’™

šŸ‘‰Review https://t.ly/qYrLZ
šŸ‘‰Paper https://arxiv.org/pdf/2607.08771
šŸ‘‰Project https://zipdepth.github.io/
šŸ‘‰Repo https://github.com/fabiotosi92/ZipDepth
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šŸ’‹SAM-MT: Real-Time Multi-Target VOSšŸ’‹

šŸ‘‰Fudan & Shangai unveil SAM-MT, an efficient interactive multi-target video segmentation framework that maintains near-single-object efficiency (FPS/VRAM) as target count increases, while maintaining robust video segmentation performance. Repo availablešŸ’™

šŸ‘‰Review https://t.ly/Z_4C7
šŸ‘‰Paper https://lnkd.in/dvS-iyBD
šŸ‘‰Project https://lnkd.in/daQ8na8T
šŸ‘‰Repo https://lnkd.in/dgbX2tZv
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šŸŒ”Foundation Global SFMšŸŒ”

šŸ‘‰Glob3R is a global SfM-style reconstruction built on 3D foundation models. key idea: explicitly optimize feed-forward geometric predictions. Repo TBAšŸ’™

šŸ‘‰Review https://t.ly/Z_4C7
šŸ‘‰Paper https://arxiv.org/pdf/2607.09225
šŸ‘‰Project https://junyuandeng.github.io/Glob3r/
šŸ‘‰Repo TBA
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šŸŽ‚REMIND: long-term MOT re-IDšŸŽ‚

šŸ‘‰REMIND by CVAR-UPM is a novel online tracker designed for long-term multi-object re-ID of generic indoor objects from monocular RGB, requiring neither camera pose nor depth. Repo under MITšŸ’™

šŸ‘‰Review https://t.ly/AkQoI
šŸ‘‰Paper https://lnkd.in/dm58mkCv
šŸ‘‰Project https://lnkd.in/dZrAZqFe
šŸ‘‰Repo https://lnkd.in/dbidrwxU
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🦧 MonkeyOCRv2 is out! 🦧

šŸ‘‰MonkeyOCRv2 is a text-centric visual foundation model that unifies fine-grained text modeling, cross-task representation learning, and cross-lingual generalization in a single encoder. Released for academic research and non-commercial usešŸ’™

šŸ‘‰Review https://t.ly/yicEK
šŸ‘‰Paper https://arxiv.org/pdf/2607.11562
šŸ‘‰Repo https://github.com/Yuliang-Liu/MonkeyOCRv2
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🌈FlowWAM: flow->action prediction🌈

šŸ‘‰FlowWAM is a novel dual-stream diffusion framework that adopts optical flow as a unified, video-native action representation. Repo under ApachešŸ’™

šŸ‘‰Review https://t.ly/FmutT
šŸ‘‰Paper https://arxiv.org/abs/2607.13017
šŸ‘‰Project https://flow-wam.github.io/
šŸ‘‰Repo github.com/YixiangChen515/FlowWAM
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šŸÆSOTA Music-to-Dance GenšŸÆ

šŸ‘‰The Tongyi Lab unveils Wan-Dancer, a novel stable minute-scale synthesis at 720p/30fps across five dance genres. Impressive results, new SOTA on long clip by a large margin. Repo under Apache 2.0šŸ’™

šŸ‘‰Review https://t.ly/AKY5j
šŸ‘‰Paper https://lnkd.in/d_xA7dwb
šŸ‘‰Project https://lnkd.in/dzfnw2h4
šŸ‘‰Repo https://lnkd.in/d-Zj_cTf
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šŸ‘‰Not a render. Not a concept. This is GENE.01 by Generative Bionics, the Italians coolest scaleup strikes back: in just six months, they turned GENE.01 into a fully functional humanoid platform that can walk, sense and interact.

šŸ‘‰Full-body multimodal skin perceives touch, proximity, force and temperature, bringing Physical AI closer to safe and natural collaboration with people.

šŸ‘‰More: https://t.ly/F3I3A
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🦜Streaming 4D Transformer🦜

šŸ‘‰IGGT4D is a novel a streaming instance-grounded geometry transformer for online 4D scene understanding. It processes video frames sequentially, reuses historical context through causal spatial-temporal modeling, and incrementally updates a unified representation of camera motion, geometry, and object identity. Repo/Data announcedšŸ’™

šŸ‘‰Review https://t.ly/LFrKR
šŸ‘‰Paper https://arxiv.org/pdf/2607.19228
šŸ‘‰Project https://iggt4d.github.io/
šŸ‘‰Repo TBA
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