Photo-real digital human avatars are of enormous importance in graphics, as they enable immersive communication over the globe, improve gaming and entertainment experiences, and can be particularly beneficial for AR and VR settings. However, current avatar generation approaches either fall short in high-fidelity novel view synthesis, generalization to novel motions, reproduction of loose clothing, or they cannot render characters at the high resolution offered by modern displays. To this end, we propose HDHumans, which is the first method for HD human character synthesis that jointly produces an accurate and temporally coherent 3D deforming surface and highly photo-realistic images of arbitrary novel views and of motions not seen at training time. At the technical core, our method tightly integrates a classical deforming character template with neural radiance fields (NeRF). Our method is carefully designed to achieve a synergy between classical surface deformation and NeRF. First, the template guides the NeRF, which allows synthesizing novel views of a highly dynamic and articulated character and even enables the synthesis of novel motions. Second, we also leverage the dense pointclouds resulting from NeRF to further improve the deforming surface via 3D-to-3D supervision. We outperform the state of the art quantitatively and qualitatively in terms of synthesis quality and resolution, as well as the quality of 3D surface reconstruction.

翻译：光真数字人类变异体在图形中具有极大的重要性,因为这些变异体能够让全球进行沉浸式交流,改善游戏和娱乐经验,并特别有益于AR和VR的设置。然而,当前变异体的代代代方法不是在高贞洁的新观点合成中短于高贞洁的新式合成,就是概括化为新运动,复制松散的衣物,或者不能在现代显示提供的高分辨率中产生字符。为此,我们提议了人文代人文,这是人文合成的第一个方法,它能够共同产生准确和时间上一致的3D变形表面和高度摄影现实的图像,以及任意的新颖观点和在培训时看不到的动作。在技术核心,我们的方法将典型变异性字符模板与神经光场(NERF)紧密整合在一起。我们的方法经过精心设计,可以实现古典表面变形与NRF提供的高分辨率之间的协同。首先,模板指导了NRF,从而可以合成高动态和清晰性能的新观点,甚至能够使新动动作合成。第二,我们还利用了高密度点质量质量的图像,通过NRF的自我变形变形,从而改进了通过NRF的3质量变形变形变形,从而改进了对质量变形。