Generalizable Neural Human Renderer
<|reference_start|>Generalizable Neural Human Renderer: 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.<|reference_end|>
@article{masuda2024generalizable,
    title={Generalizable Neural Human Renderer},
    author={Mana Masuda, Jinhyung Park, Shun Iwase, Rawal Khirodkar, Kris Kitani},
    journal={arXiv preprint arXiv:2404.14199},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14199},
    primaryClass={cs.CV}
}