CCR: Facial Image Editing with Continuity, Consistency and Reversibility

Nan Yang, Xin Luan, Huidi Jia, Zhi Han, Xiaofeng Li, Yandong Tang

Abstract: Three problems exist in sequential facial image editing: discontinuous editing, inconsistent editing, and irreversible editing. Discontinuous 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 their corresponding definitions: editing continuity, consistency, and reversibility. Note that continuity refers to the continuity of attributes, that is, attributes can be continuously edited on any face. Consistency is that not only attributes meet continuity, but also facial identity needs to be consistent. To do so, we propose a novel model to achieve the goal of editing continuity, consistency, and reversibility. Furthermore, 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. We believe 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.

External IDs:dblp:journals/ijcv/YangLJHLT24