CCR: Facial Image Editing with Continuity, Consistency and Reversibility

TL;DR

This paper introduces a new model for facial image editing that ensures edits are continuous, consistent, and reversible, addressing key issues in sequential editing to improve flexibility and identity preservation.

Contribution

The paper proposes novel definitions and a model for continuous, consistent, and reversible facial image editing, enhancing editing flexibility and identity retention.

Findings

Model achieves continuous, consistent, and reversible editing.

Experimental results validate improved editing flexibility.

Preserves facial identity while enabling sequential edits.

Abstract

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.