论文标题
CCR：具有连续性，一致性和可逆性的面部图像编辑
CCR: Facial Image Editing with Continuity, Consistency and Reversibility
论文作者
论文摘要
顺序面部图像编辑中存在三个问题：不连续的编辑，不一致的编辑和不可逆的编辑。不连续的编辑是当前的编辑无法保留先前编辑的属性。不一致的编辑是交换属性编辑订单不能产生相同的结果。不可逆转的编辑意味着在面部图像上操作是不可逆的，尤其是在顺序的面部图像编辑中。在这项工作中，我们提出了三个概念和相应的定义：编辑连续性，一致性和可逆性。然后，我们提出了一个新型模型，以实现编辑连续性，一致性和可逆性的目标。定义了足够的标准以确定模型是否是连续，一致和可逆的。广泛的定性和定量实验结果验证了我们提出的模型，并表明连续，一致和可逆的编辑模型具有更灵活的编辑功能，同时保留面部身份。此外，我们认为我们提出的定义和模型将在多媒体处理中具有广泛而有希望的应用。代码和数据可在https://github.com/mickoluan/ccr上找到。
Three problems exist in sequential facial image editing: incontinuous editing, inconsistent editing, and irreversible editing. Incontinuous editing is that the current editing can not retain the previously edited attributes. Inconsistent editing is that swapping the attribute editing orders can not yield the same results. Irreversible editing means that operating on a facial image is irreversible, especially in sequential facial image editing. In this work, we put forward three concepts and corresponding definitions: editing continuity, consistency, and reversibility. Then, we propose a novel model to achieve the goal of editing continuity, consistency, and reversibility. A sufficient criterion is defined to determine whether a model is continuous, consistent, and reversible. Extensive qualitative and quantitative experimental results validate our proposed model and show that a continuous, consistent and reversible editing model has a more flexible editing function while preserving facial identity. Furthermore, we think that our proposed definitions and model will have wide and promising applications in multimedia processing. Code and data are available at https://github.com/mickoluan/CCR.
