Human body volume estimation from a single RGB image is a challenging problem despite minimal attention from the research community. However VolNet, an architecture leveraging 2D and 3D pose estimation, body part segmentation and volume regression extracted from a single 2D RGB image combined with the subject's body height can be used to estimate the total body volume. VolNet is designed to predict the 2D and 3D pose as well as the body part segmentation in intermediate tasks. We generated a synthetic, large-scale dataset of photo-realistic images of human bodies with a wide range of body shapes and realistic poses called SURREALvols. By using Volnet and combining multiple stacked hourglass networks together with ResNeXt, our model correctly predicted the volume in ~82% of cases with a 10% tolerance threshold. This is a considerable improvement compared to state-of-the-art solutions such as BodyNet with only a ~38% success rate.
翻译：尽管研究界很少注意RGB图像,但从单一的 RGB 图像中估算人体体积仍是一个棘手的问题。 然而,VolNet 是一个利用 2D 和 3D 的架构,它提出了估计,从单一的 2D RGB 图像中提取的体分解和体积回归与主体体身高度相结合,可用于估计体积总量。VolNet 旨在预测 2D 和 3D 的体体积以及中间任务中的体积分割。我们生成了一个合成的、大型的人体体形和现实姿势图像数据集,称为 SURREALVLs。通过使用Volnet 和将多个堆叠式沙子网络与ResNeXt 相结合,我们的模型正确地预测了大约82%的病例数量,其中的容积临界值为10%。这与人体网等最先进的解决方案相比有很大的改进,只有~38%的成功率。
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