Showing 4 open source projects for "network render"

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    Apache Log4j

    Apache Log4j

    Apache Log4j 2 is a versatile, feature-rich, efficient logging API

    Apache Log4j is a versatile, industrial-grade Java logging framework composed of an API, its implementation, and components to assist the deployment for various use cases. Log4j is used by 8% of the Maven ecosystem and listed as one of the top 100 critical open source software projects. The project is actively maintained by a team of several volunteers and supported by a big community.
    Downloads: 9 This Week
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  • 2
    Front-End Performance Checklist

    Front-End Performance Checklist

    The only Front-End Performance Checklist that runs faster

    ...It frames performance as a front-end responsibility and turns broad principles into concrete, verifiable checks you can apply before shipping. The checklist is organized by areas such as HTML, CSS, JavaScript, fonts, images, and network delivery, so teams can tackle bottlenecks systematically. Each section emphasizes practical outcomes like cutting render-blocking resources, optimizing critical path CSS, and leveraging modern compression and caching. The project is designed to be used in code reviews and pre-launch audits, making it easy to institutionalize performance culture across teams. ...
    Downloads: 0 This Week
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  • 3
    sigma.js

    sigma.js

    A JavaScript library dedicated to graph drawing

    ...Sigma provides a lot of different settings to make it easy to customize how to draw and interact with networks. And you can also directly add your own functions to your scripts to render nodes and edges the exact way you want. Sigma is a rendering engine, and it is up to you to add all the interactivity you want.
    Downloads: 1 This Week
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  • 4
    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: 1 This Week
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