Showing 4 open source projects for "open directory"

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    Menagerie

    Menagerie

    A collection of high-quality models for the MuJoCo physics engine

    MuJoCo Menagerie, developed by Google DeepMind, is a curated collection of high-quality simulation models designed for use with the MuJoCo physics engine. It serves as a comprehensive library of accurate and ready-to-use robotic, biomechanical, and mechanical models, ensuring users can perform reliable simulations without having to build or tune models from scratch. The repository aims to improve reproducibility and quality across robotics research by providing verified models that adhere to...
    Downloads: 1 This Week
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  • 2
    Wardrobe

    Wardrobe

    Your clothes, extracted and organized with gpt-image

    Wardrobe is a local wardrobe organizer that uses OpenAI image models to extract and present clothing from personal photos. It detects garments in imported images, creates clean product-style cutouts, and can generate modeled editorial previews using a reference photo. The web interface supports drag-and-drop, paste, editing, review, regeneration, and approval of generated results. Original images, processed assets, job data, and the JSON clothing library remain in a local data directory. Two...
    Downloads: 0 This Week
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  • 3
    OpenAI Realtime Embedded

    OpenAI Realtime Embedded

    Instructions on how to use the Realtime API on Microcontrollers

    openai-realtime-embedded is a repository that provides resources, SDKs, and example links for using OpenAI’s Realtime API on embedded hardware platforms (e.g. microcontrollers). The goal is to enable low-latency conversational agents (e.g. voice-based assistants) running directly on constrained devices, by leveraging WebRTC and streaming APIs to communicate with OpenAI systems. The repo includes pointers to an ESP32 implementation (maintained as esp32 branch) and documentation that Espressif...
    Downloads: 0 This Week
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  • 4
    DeepSDF

    DeepSDF

    Learning Continuous Signed Distance Functions for Shape Representation

    DeepSDF is a deep learning framework for continuous 3D shape representation using Signed Distance Functions (SDFs), as presented in the CVPR 2019 paper DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation by Park et al. The framework learns a continuous implicit function that maps 3D coordinates to their corresponding signed distances from object surfaces, allowing compact, high-fidelity shape modeling. Unlike traditional discrete voxel grids or meshes, DeepSDF...
    Downloads: 1 This Week
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