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arXiv · 2404.14199

Generalizable Neural Human Renderer

Mana Masuda·Jinhyung Park·Shun Iwase·Rawal Khirodkar·Kris Kitani
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Abstract

While recent advancements in animatable human rendering have achieved remarkable results, they require test-time optimization for each subject which can be a significant limitation for real-world applications. To address this, we tackle the challenging task of learning a Generalizable Neural Human Renderer (GNH), a novel method for rendering animatable humans from monocular video without any test-time optimization. Our core method focuses on transferring appearance information from the input video to the output image plane by utilizing explicit body priors and multi-view geometry. To render the subject in the intended pose, we utilize a straightforward CNN-based image renderer, foregoing the more common ray-sampling or rasterizing-based rendering modules. Our GNH achieves remarkable generalizable, photorealistic rendering with unseen subjects with a three-stage process. We quantitatively and qualitatively demonstrate that GNH significantly surpasses current state-of-the-art methods, notably achieving a 31.3% improvement in LPIPS.

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Mana Masuda, Jinhyung Park, Shun Iwase, Rawal Khirodkar, Kris Kitani. 2024-04-22. Generalizable Neural Human Renderer. https://arxiv.org/abs/2404.14199

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