We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D
human reconstruction from a single RGB image. To reduce the ambiguities
associated with the surface geometry reconstruction, even for the
reconstruction of invisible areas, we propose and leverage a dense semantic
representation generated from SMPL model as an additional input. One key
feature of our network is that it fuses different scales of image features into
the 3D space through volumetric feature transformation, which helps to recover
accurate surface geometry. The visible surface details are further refined
through a normal refinement network, which can be concatenated with the volume
generation network using our proposed volumetric normal projection layer. We
also contribute THuman, a 3D real-world human model dataset containing about
7000 models. The network is trained using training data generated from the
dataset. Overall, due to the specific design of our network and the diversity
in our dataset, our method enables 3D human model estimation given only a
single image and outperforms state-of-the-art approaches.

提出了一种用于从单个 RGB 图像进行 3D 人体重建的基于图像引导的体积到体积的转换 CNN，通过 SMPL 模型生成密集的语义表示来减少与表面几何重建相关的歧义，将不同尺度的图像特征融合到三维空间中，并通过法线细化网络来进一步提高可见面细节的精度，使用所提出的体积法向投影层将其连接到体积生成网络中，并使用其中包含的 3D 真实世界人体模型数据集进行训练，实验表明该方法在前沿方法中表现优异。