Realistic virtual humans play a crucial role in numerous industries, such as metaverse, intelligent healthcare, and self-driving simulation. But creating them on a large scale with high levels of realism remains a challenge. The utilization of deep implicit function sparks a new era of image-based 3D clothed human reconstruction, enabling pixel-aligned shape recovery with fine details. Subsequently, the vast majority of works locate the surface by regressing the deterministic implicit value for each point. However, should all points be treated equally regardless of their proximity to the surface? In this paper, we propose replacing the implicit value with an adaptive uncertainty distribution, to differentiate between points based on their distance to the surface. This simple ``value to distribution'' transition yields significant improvements on nearly all the baselines. Furthermore, qualitative results demonstrate that the models trained using our uncertainty distribution loss, can capture more intricate wrinkles, and realistic limbs. Code and models are available for research purposes at https://github.com/psyai-net/D-IF_release.

翻译：逼真的虚拟角色在元宇宙、智能医疗和自动驾驶仿真等众多行业中扮演着关键角色。然而，大规模生成高真实感的虚拟人体仍是一项挑战。深度隐式函数的应用开创了基于图像的3D着装人体重建的新时代，实现了像素级对齐的细粒度形状恢复。随后，绝大多数工作通过回归每个点的确定性隐式值来定位表面。但所有点是否应依据其与表面的距离被平等对待？本文提出用自适应不确定性分布替代隐式值，以区分不同点到表面的距离差异。这种简单的"值到分布"转换在几乎所有基线方法上均取得了显著改进。此外，定性结果表明，采用我们的不确定性分布损失训练的模型能够捕捉更复杂的褶皱和逼真的肢体。代码和模型已开源供研究使用：https://github.com/psyai-net/D-IF_release。