基于表情传输的交互式照片编辑

目的 生活中照片拍摄时难以捕捉到所有人脸表情最佳的时刻,多次摆拍不仅费时,而且可能会错过某些场景,传统的后期编辑软件不具备针对性,且操作复杂。针对人物照片中部分人脸表情不佳的情况,提出一种基于表情传输的交互式照片编辑算法。方法 首先将包含源人脸的照片与具有目标表情人脸的照片进行特征点检测,通过交互将指定人脸部分选出并将其姿态归正为眼睛在同一水平线上,如果目标人脸与源人脸身份相同,将目标人脸区域根据源人脸的轮廓以及左右半脸分布以扫描线变形得到替换目标;不相同时根据目标人脸特征点分布的几何特征计算源人脸中特征点的新位置,通过基于特征点变化的网格变形得到替换目标,最后利用二次光照与泊松融合将其无缝拼接到源图中。结果 实验表明算法可以对人脸五官清晰且在宽容度内的人物照片进行表情编辑,处理结果只改变了人物的脸部表情,并且无明显拼接痕迹。结论 提出了一种新型针对目标人脸不同身份信息的交互式表情传输模型,该模型可以适应不同的编辑条件与要求,效果出色。

Interactive photo editing based on expression transmission

Objective Seldom does a photograph record what we perceive with our eyes. As a result, we could not obtain an optimal photo with all the subjects smiling as expected. When taking pictures, we spend a considerable amount of time capturing other shots and inadvertently miss beautiful scenes. Traditional photo editing apps could not facilitate expression editing. Hence, we develop an algorithm for interactive photo editing based on expression transmission to edit parts of faces with poor expression. The proposed algorithm uses scan line deformation or feature point deformation on the basis of the identity related to the source face. Method Our approach is to utilize target face expression to edit source faces with unsatisfied expressions. The result shares an expression similar to that of the target face. The face is edited by relighting and then finally fused in the photo. Result The proposed algorithm can edit frontal face images to achieve desired expressions. This algorithm, which is based on different processing conditions and transmission models, demonstrate better performance than the conventional Gaussian mixture model with regard to the test results. Conclusion This study proposes a novel expression transmission model. We assign different transmission models for different pieces of target face identity information and subsequently introduce interaction. Experiment results indicate that our model achieves preferable adaptive performance when applied in photos with different conditions and requirements.