We present a simple yet effective 3D convolutional neural network that learns a novel spacetime representation for human contour detection in a sequence of images. Our approach, in one shot, detects the contour of humans while generating high-quality results compared to the traditional binary mask representations. Our time-consistent convolutional neural network takes a sequence of images as its input and generates an implicit level set surface, in which the object boundaries correspond to the zero-level set. Furthermore, we introduce an appropriate way to combine space and time in an interwoven coordinate system tailored to spatiotemporal datasets. We showcase the feasibility of our approach by training the network on a semi-synthetic dataset. We discuss various configurations of our approach, all of which is shown to outperform the typical binary mask representation. We believe that this new approach could potentially improve the performance of all architectures compared to their alternative ones. The code will be made available on https://github.com/orgs/OSUPCVLab/HumanContourDetection.

我们提出了一个简单而有效的3D卷积神经网络，该网络学习了一系列图像中人轮廓检测的新颖时空表示形式。与传统的二进制蒙版表示法相比，我们的方法可以一口气检测出人的轮廓，同时生成高质量的结果。我们的时间一致卷积神经网络将图像序列作为输入，并生成一个隐含的水平集表面，其中对象边界对应于零水平集。此外，我们介绍了一种适合时空数据集的交织坐标系中将空间和时间结合起来的合适方法。我们通过在半合成数据集上训练网络来展示我们方法的可行性。我们讨论了我们方法的各种配置，所有这些都优于典型的二进制掩码表示。我们认为，与其他架构相比，这种新方法可能会改善所有架构的性能。该代码将在https://github.com/orgs/OSUPCVLab/HumanContourDetection上可用。