Abstract:

The human visual attention mechanism shows that when the human eyes look at the target, the attention will only be focused on few areas of interest, while most of the other areas out of interest in the field of vision will be automatically ignored. The study of human visual attention mechanism and the construction of an effective clothing saliency prediction model can be used to guide more realistic and effective clothing motion modeling and improve the efficiency of simulation. In this paper, we analyzed the video data of the dressed human movement, constructed a variety of video samples, and adopted eye movement technology to collect the gaze data of real human eyes. Gauss convolution was used to generate the salient image of video frame as the Ground-truth required for training model. In the video feature extraction, the underlying image features, high-level semantic features and motion features were combined to construct feature vectors and tags, and the significance prediction model based on clothing video was obtained by support vector machine (SVM) training. The experimental results show that the proposed method outperforms the traditional significance prediction algorithm and hassome robustness in clothing saliency prediction.

Key words:  visual attention mechanism, clothing modeling, eye movement technique, SVM, saliency prediction