We present a learning-based model to infer the personalized 3D shape of
people from a few frames (1-8) of a monocular video in which the person is
moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our
model learns to predict the parameters of a statistical body model and instance
displacements that add clothing and hair to the shape. The model achieves fast
and accurate predictions based on two key design choices. First, by predicting
shape in a canonical T-pose space, the network learns to encode the images of
the person into pose-invariant latent codes, where the information is fused.
Second, based on the observation that feed-forward predictions are fast but do
not always align with the input images, we predict using both, bottom-up and
top-down streams (one per view) allowing information to flow in both
directions. Learning relies only on synthetic 3D data. Once learned, the model
can take a variable number of frames as input, and is able to reconstruct
shapes even from a single image with an accuracy of 6mm. Results on 3 different
datasets demonstrate the efficacy and accuracy of our approach.

本文提出了一种基于学习的模型，利用少量单目视频帧 (1-8 帧) 从移动的人中推断出其个性化的 3D 形状，可在不到 10 秒内达到 5mm 的重建精度。该模型学习预测一个统计体型模型的参数和实例化偏移量，同时加入衣服和头发的形状，具有快速和准确的预测，这得益于其对 T-pose 空间的预测和从上往下和从下往上两个视图预测的结合。该模型仅基于合成的 3D 数据进行学习，可将可变数量的帧作为输入，即使仅有一张图像，也能以 6mm 的精度重建形状。3 个不同数据集的结果表明了本方法的有效性和准确性。