Showing 3 open source projects for "crisp"

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    Sprite Fusion Pixel Snapper

    Sprite Fusion Pixel Snapper

    A tool to snap pixels to a perfect grid

    ...The tool works by adjusting sprite rendering coordinates and texture sampling so that every pixel aligns cleanly to the screen’s pixel grid, avoiding blurring, distortion, or unintended smoothing artifacts. This is especially important in pixel art games, retro-styled interactive media, or precise UI designs where crisp edges and predictable alignment are essential. SpriteFusion Pixel Snapper integrates with popular game engines and rendering pipelines to ensure that assets remain sharp across a broad range of resolutions and aspect ratios without requiring manual fiddling from artists or developers. It includes options for snapping modes, filtering overrides, and automatic correction detection that can be applied on export or at runtime.
    Downloads: 7 This Week
    Last Update:
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  • 2
    Depth Pro

    Depth Pro

    Sharp Monocular Metric Depth in Less Than a Second

    ...Apple highlights both accuracy and speed: the model can synthesize a ~2.25-megapixel depth map in around 0.3 seconds on a standard GPU, enabling near real-time applications. The repo and research page emphasize boundary fidelity and crisp geometry, addressing a common weakness in monocular depth where edges can blur. Community integrations (e.g., inference wrappers and UI nodes) have sprung up around the model, reflecting practical interest in video, AR, and generative pipelines. As a general-purpose monocular depth backbone, Depth Pro slots into 3D reconstruction, relighting, and scene understanding workflows that benefit from metric predictions.
    Downloads: 7 This Week
    Last Update:
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  • 3
    DeepMask

    DeepMask

    Torch implementation of DeepMask and SharpMask

    DeepMask is an early, influential approach to class-agnostic object segmentation that learns to propose pixel-accurate masks directly from images. Instead of first generating boxes and then refining them, the network predicts a foreground mask and an “objectness” score for a given image patch, yielding high-quality segment proposals suitable for downstream detection or instance segmentation. The model is trained end-to-end to align mask shape with object extent, which markedly improves...
    Downloads: 0 This Week
    Last Update:
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