unitedhuman.github.io - UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation

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Description: UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation

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Human generation has achieved significant progress. Nonetheless, existing methods still struggle to synthesize specific regions such as faces and hands. We argue that the main reason is rooted in the training data. A holistic human dataset inevitably has insufficient and low-resolution information on local parts. Therefore, we propose to use multi-source datasets with various resolution images to jointly learn a high-resolution human generative model. However, multi-source data inherently a) contains differ

Here are the comparison results of StyleGAN-Human, InsetGAN, AnyRes, and UnitedHuman. We exhibit the full-body human images generated from each experiment at a resolution of 1024 ( ), as well as the face and hand patches cut from the 2048px images ( ).

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Links to unitedhuman.github.io (1)

• yumingj.github.io Yuming Jiang - Homepage