Showing 4 open source projects for "speech synthesis source code"

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  • 1
    DeepMind Research

    DeepMind Research

    Implementations and code to accompany DeepMind publications

    This repository collects reference implementations and illustrative code accompanying a wide range of DeepMind publications, making it easier for the research community to reproduce results, inspect algorithms, and build on prior work. The top level organizes many paper-specific directories across domains such as deep reinforcement learning, self-supervised vision, generative modeling, scientific ML, and program synthesis—for example BYOL, Perceiver/Perceiver IO, Enformer for genomics,...
    Downloads: 2 This Week
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  • 2
    NeuMan

    NeuMan

    Neural Human Radiance Field from a Single Video (ECCV 2022)

    NeuMan is a reference implementation that reconstructs both an animatable human and its background scene from a single monocular video using neural radiance fields. It supports novel view and novel pose synthesis, enabling compositional results like transferring reconstructed humans into new scenes. The pipeline separates human/body and environment, learning consistent geometry and appearance to support animation. Demos showcase sequences such as dance and handshake, and the code provides...
    Downloads: 0 This Week
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  • 3
    Nerfies

    Nerfies

    This is the code for Deformable Neural Radiance Fields

    Nerfies demonstrates deformation-aware neural radiance fields that reconstruct and render dynamic, real-world scenes from casual video. Instead of assuming a static world, the method learns a canonical space plus a deformation field that maps changing poses or expressions back to that space during training. This lets the system generate photorealistic novel views of nonrigid subjects—faces, bodies, cloth—while preserving fine detail and consistent lighting. The training pipeline handles...
    Downloads: 0 This Week
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  • 4
    Tensor2Tensor

    Tensor2Tensor

    Library of deep learning models and datasets

    Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and...
    Downloads: 0 This Week
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