Human shape estimation is an important task for video editing, animation and
fashion industry. Predicting 3D human body shape from natural images, however,
is highly challenging due to factors such as variation in human bodies,
clothing and viewpoint. Prior methods addressing this problem typically attempt
to fit parametric body models with certain priors on pose and shape. In this
work we argue for an alternative representation and propose BodyNet, a neural
network for direct inference of volumetric body shape from a single image.
BodyNet is an end-to-end trainable network that benefits from (i) a volumetric
3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate
supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them
results in performance improvement as demonstrated by our experiments. To
evaluate the method, we fit the SMPL model to our network output and show
state-of-the-art results on the SURREAL and Unite the People datasets,
outperforming recent approaches. Besides achieving state-of-the-art
performance, our method also enables volumetric body-part segmentation.

本篇研究提出了一种名为 BodyNet 的神经网络，通过直接推断体积棱柿，从而预测自然图像中的三维人体形状，同时结合三维损失和多视角重投影损失以及适当的监督，实现了性能的提升；通过将 SMPL 模型应用于网络输出，并在 SURREAL 和 Unite the People 数据集上展示了最先进的结果，证明了本方法的有效性。