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πͺ΄ Pixie: Physics from Pixels πͺ΄
πUPenn + MIT unveil Pixie: training a neural-net that maps pretrained visual features (i.e., CLIP) to dense material fields of physical properties in a single forward pass, enabling realβtime physics simulations. Repo & Dataset under MIT licenseπ
πReview https://t.ly/1W0n5
πPaper https://lnkd.in/dsHAHDqM
πProject https://lnkd.in/dwrHRbRc
πRepo https://lnkd.in/dy7bvjsK
πUPenn + MIT unveil Pixie: training a neural-net that maps pretrained visual features (i.e., CLIP) to dense material fields of physical properties in a single forward pass, enabling realβtime physics simulations. Repo & Dataset under MIT licenseπ
πReview https://t.ly/1W0n5
πPaper https://lnkd.in/dsHAHDqM
πProject https://lnkd.in/dwrHRbRc
πRepo https://lnkd.in/dy7bvjsK
β€5π2π₯1
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π«TMR: Few-Shot Template-matchingπ«
πPOSTECH unveils TMR, a novel and simple template-matching detector for few-shot pattern detection, achieving strong (and SOTA) results on diverse datasets. A new dataset (RPINE) released, repo soonπ
πReview https://t.ly/WWAcL
πPaper https://lnkd.in/dJbSu5vk
πProject https://lnkd.in/dwcDnHHQ
πRepo https://lnkd.in/dp7aw8Cs
πPOSTECH unveils TMR, a novel and simple template-matching detector for few-shot pattern detection, achieving strong (and SOTA) results on diverse datasets. A new dataset (RPINE) released, repo soonπ
πReview https://t.ly/WWAcL
πPaper https://lnkd.in/dJbSu5vk
πProject https://lnkd.in/dwcDnHHQ
πRepo https://lnkd.in/dp7aw8Cs
π₯5β€3π1
𧬠OpenVision 2 is out! π§¬
πUCSC releases OpenVision2: a novel family of generative pretrained visual encoders that removes the text encoder and contrastive loss, training with caption-only supervision. Fully open, Apache 2.0π
πReview https://t.ly/Oma3w
πPaper https://arxiv.org/pdf/2509.01644
πProject https://ucsc-vlaa.github.io/OpenVision2/
πRepo https://github.com/UCSC-VLAA/OpenVision
πUCSC releases OpenVision2: a novel family of generative pretrained visual encoders that removes the text encoder and contrastive loss, training with caption-only supervision. Fully open, Apache 2.0π
πReview https://t.ly/Oma3w
πPaper https://arxiv.org/pdf/2509.01644
πProject https://ucsc-vlaa.github.io/OpenVision2/
πRepo https://github.com/UCSC-VLAA/OpenVision
π₯7β€1π1
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π #DoubleDragon with #AI π
πHow Double Dragon would look like in real life? Each character has been transformed with #AI to capture their style, fighting spirit, and charisma, as if they had stepped right out of the gameβs streets into the real world. AUDIO ON. Damn romanticπ
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse #LLM
πPost https://t.ly/0IpER
πChannel http://www.youtube.com/@iaiaoh84
πHow Double Dragon would look like in real life? Each character has been transformed with #AI to capture their style, fighting spirit, and charisma, as if they had stepped right out of the gameβs streets into the real world. AUDIO ON. Damn romanticπ
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse #LLM
πPost https://t.ly/0IpER
πChannel http://www.youtube.com/@iaiaoh84
β€5π2π₯1
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π Promptable Human Mesh π
πPromptHMR is a promptable human pose/shape (HPS) estimation method that processes images with spatial or semantic prompts. It takes βside informationβ readily available from vision-language models or user input to improve the accuracy and robustness of 3D HPS. Code releasedπ
πReview https://t.ly/zJ7S-
πPaper arxiv.org/pdf/2504.06397
πProject yufu-wang.github.io/phmr-page/
πRepo github.com/yufu-wang/PromptHMR
πPromptHMR is a promptable human pose/shape (HPS) estimation method that processes images with spatial or semantic prompts. It takes βside informationβ readily available from vision-language models or user input to improve the accuracy and robustness of 3D HPS. Code releasedπ
πReview https://t.ly/zJ7S-
πPaper arxiv.org/pdf/2504.06397
πProject yufu-wang.github.io/phmr-page/
πRepo github.com/yufu-wang/PromptHMR
π€£18β€10π1π₯1
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π₯WebEyeTrack: real-time/web eyeπ₯
πWebEyeTrack is a novel framework that integrates lightweight SOTA gaze estimation models directly in the browser. Bringing deepβlearning gaze estimation to the web browser and explicitly accounts for head pose. Source Code released under MIT licenseπ
πReview https://t.ly/Xon9h
πPaper https://arxiv.org/pdf/2508.19544
πProject redforestai.github.io/WebEyeTrack/
πRepo github.com/RedForestAi/WebEyeTrack
πWebEyeTrack is a novel framework that integrates lightweight SOTA gaze estimation models directly in the browser. Bringing deepβlearning gaze estimation to the web browser and explicitly accounts for head pose. Source Code released under MIT licenseπ
πReview https://t.ly/Xon9h
πPaper https://arxiv.org/pdf/2508.19544
πProject redforestai.github.io/WebEyeTrack/
πRepo github.com/RedForestAi/WebEyeTrack
π₯8β€3π1
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βοΈ AI Open-Source Annotation βοΈ
πVisioFirm by TOELT is a fully open-source, AI-powered image annotation tool designed to accelerate labeling for Computer Vision tasks like object detection, oriented BBs, and segmentation. Source code released under Apache 2.0π
πReview https://t.ly/MoMvv
πPaper https://lnkd.in/dxTncSgv
πRepo https://lnkd.in/dCWMXp3x
πVisioFirm by TOELT is a fully open-source, AI-powered image annotation tool designed to accelerate labeling for Computer Vision tasks like object detection, oriented BBs, and segmentation. Source code released under Apache 2.0π
πReview https://t.ly/MoMvv
πPaper https://lnkd.in/dxTncSgv
πRepo https://lnkd.in/dCWMXp3x
π₯11π€―4β€3π3β‘1
Friends,
Iβve just open my IG account: https://www.instagram.com/aleferra.ig | Feel free to add me
What about posting stuff about AI on IG? Thoughts?
Iβve just open my IG account: https://www.instagram.com/aleferra.ig | Feel free to add me
What about posting stuff about AI on IG? Thoughts?
π11π€―1
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ποΈReal-Time Drag-Based EditingποΈ
πThe Visual AI Lab unveils Inpaint4Drag, a novel framework that decomposes drag-based editing into pixel-space bidirectional warping/inpainting. Inspired by elastic object deformation. Demo and Code released (unknown license)π
πReview https://t.ly/H5nlR
πPaper https://arxiv.org/pdf/2509.04582
πProject https://visual-ai.github.io/inpaint4drag/
πRepo https://github.com/Visual-AI/Inpaint4Drag
πDemo https://colab.research.google.com/drive/1fzoyNzcJNZjM1_08FE9V2V20EQxGf4PH
πThe Visual AI Lab unveils Inpaint4Drag, a novel framework that decomposes drag-based editing into pixel-space bidirectional warping/inpainting. Inspired by elastic object deformation. Demo and Code released (unknown license)π
πReview https://t.ly/H5nlR
πPaper https://arxiv.org/pdf/2509.04582
πProject https://visual-ai.github.io/inpaint4drag/
πRepo https://github.com/Visual-AI/Inpaint4Drag
πDemo https://colab.research.google.com/drive/1fzoyNzcJNZjM1_08FE9V2V20EQxGf4PH
β€6π₯6π1
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π©ΈFoundation Red Blood Cellsπ©Έ
πRedDino from University of Cagliari is a self-supervised foundation model designed for red blood cell (RBC) morphology analysis. Trained on 1.25M RBC images, it's the new SOTA in shape classification. Code & Models released under Apache2.0π
πReview https://t.ly/uWAch
πPaper arxiv.org/pdf/2508.08180
πCode github.com/Snarci/RedDino
πModels huggingface.co/collections/Snarcy/reddino-689a13e29241d2e5690202fc
πRedDino from University of Cagliari is a self-supervised foundation model designed for red blood cell (RBC) morphology analysis. Trained on 1.25M RBC images, it's the new SOTA in shape classification. Code & Models released under Apache2.0π
πReview https://t.ly/uWAch
πPaper arxiv.org/pdf/2508.08180
πCode github.com/Snarci/RedDino
πModels huggingface.co/collections/Snarcy/reddino-689a13e29241d2e5690202fc
β€15π4π₯3
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π» From Skin to Skeleton π»
πThis paper try unifying the SMPL body model with BSM, a new Biomechanical Skeleton Model. The SKEL model is animatable like SMPL but with fewer, and biomechanically-realistic, degrees of freedom. Model, code, and data available for researchπ
πReview https://t.ly/JsI8M
πPaper arxiv.org/pdf/2509.06607
πProject https://skel.is.tue.mpg.de/
πThis paper try unifying the SMPL body model with BSM, a new Biomechanical Skeleton Model. The SKEL model is animatable like SMPL but with fewer, and biomechanically-realistic, degrees of freedom. Model, code, and data available for researchπ
πReview https://t.ly/JsI8M
πPaper arxiv.org/pdf/2509.06607
πProject https://skel.is.tue.mpg.de/
β€1π1π₯1