Showing 7 open source projects for "video capture linux mint"

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  • 1
    InternVL

    InternVL

    A Pioneering Open-Source Alternative to GPT-4o

    InternVL is a large-scale multimodal foundation model designed to integrate computer vision and language understanding within a unified architecture. The project focuses on scaling vision models and aligning them with large language models so that they can perform tasks involving both visual and textual information. InternVL is trained on massive collections of image-text data, enabling it to learn representations that capture both visual patterns and semantic meaning. The model supports a...
    Downloads: 0 This Week
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  • 2
    FrankMocap

    FrankMocap

    A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator

    FrankMocap is a monocular 3D human capture system that estimates body, hand, and optionally face pose from a single RGB image or video. It regresses parametric human models (e.g., SMPL/SMPL-X) directly, producing temporally stable meshes and joint angles suitable for animation or analytics. The pipeline couples a robust 2D keypoint detector with 3D mesh regression networks and priors that keep results anatomically plausible. It can run frame-by-frame or with temporal smoothing, and includes...
    Downloads: 2 This Week
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  • 3
    TimeSformer

    TimeSformer

    The official pytorch implementation of our paper

    TimeSformer is a vision transformer architecture for video that extends the standard attention mechanism into spatiotemporal attention. The model alternates attention along spatial and temporal dimensions (or designs variants like divided attention) so that it can capture both appearance and motion cues in video. Because the attention is global across frames, TimeSformer can reason about dependencies across long time spans, not just local neighborhoods. The official implementation in PyTorch...
    Downloads: 0 This Week
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  • 4
    DensePose

    DensePose

    A real-time approach for mapping all human pixels of 2D RGB images

    DensePose is a computer vision system that maps all human pixels in an RGB image to the 3D surface of a human body model. It extends human pose estimation from predicting joint keypoints to providing dense correspondences between 2D images and a canonical 3D mesh (such as the SMPL model). This enables detailed understanding of human shape, motion, and surface appearance directly from images or videos. The repository includes the DensePose network architecture, training code, pretrained...
    Downloads: 5 This Week
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  • 5
    Linux-Intelligent-Ocr-Solution

    Linux-Intelligent-Ocr-Solution

    Easy-OCR solution and Tesseract trainer for GNU/Linux

    Linux-intelligent-ocr-solution Lios is a free and open source software for converting print in to text using either scanner or a camera, It can also produce text out of scanned images from other sources such as Pdf, Image, Folder containing Images or screenshot. Program is given total accessibility for visually impaired. A Tesseract Trainer GUI is also shipped with this package. Forum : https://groups.google.com/forum/#!forum/lios Video Tutorial :...
    Downloads: 14 This Week
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  • 6
    VideoPose3D

    VideoPose3D

    Efficient 3D human pose estimation in video using 2D keypoint

    VideoPose3D is a deep learning framework that reconstructs 3D human poses from 2D keypoint sequences extracted from videos. It builds on top of convolutional and temporal networks that map 2D joint coordinates over time to consistent 3D skeletons, enabling robust motion capture without specialized sensors. The model is trained on large motion capture datasets and can generalize well to unseen environments by leveraging temporal context for smoothing and error correction. By using only 2D...
    Downloads: 1 This Week
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  • 7
    Concurrent vision processing system; Toolkit for easy implementation of software concurrent vision processing sub-system. Aimed at robotic applications w/best effort realtime. System includes: capture,processing and displaying.
    Downloads: 0 This Week
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