This paper presents a new framework for human body part segmentation based on Deep Convolutional Neural Networks trained using only synthetic data. The proposed approach achieves cutting-edge results without the need of training the models with real annotated data of human body parts. Our contributions include a data generation pipeline, that exploits a game engine for the creation of the synthetic data used for training the network, and a novel pre-processing module, that combines edge response maps and adaptive histogram equalization to guide the network to learn the shape of the human body parts ensuring robustness to changes in the illumination conditions. For selecting the best candidate architecture, we perform exhaustive tests on manually annotated images of real human body limbs. We further compare our method against several high-end commercial segmentation tools on the body parts segmentation task. The results show that our method outperforms the other models by a significant margin. Finally, we present an ablation study to validate our pre-processing module. With this paper, we release an implementation of the proposed approach along with the acquired datasets.
翻译:本文介绍了基于仅使用合成数据的深革命神经网络所培训的人体分解新框架。拟议方法在不需要用人体部分真实附加说明的数据对模型进行培训的情况下取得了尖端成果。我们的贡献包括数据生成管道,利用一个游戏引擎来创建用于培训网络的合成数据,以及一个新的预处理模块,将边缘反应图和适应性直方图均匀性结合起来,以指导网络学习人体部分的形状,以确保对照明条件变化的稳健性。在选择最佳候选结构时,我们对人的身体肢体的手动附加说明图像进行详尽测试。我们进一步比较了我们的方法与身体部分分解任务上的若干高端商业分解工具。结果显示,我们的方法大大超越了其他模型。最后,我们介绍了一个模拟研究,以验证我们的预处理模块。我们通过这一论文,在获取的数据集中公布了拟议方法的实施情况。