CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing

TL;DR

CA-Edit introduces a causality-aware approach with a novel data strategy and skin transition guidance to improve high-fidelity, localized facial attribute editing, addressing issues of detail preservation and alignment.

Contribution

The paper presents a new causality-aware condition adapter and data utilization strategy that enhance facial detail preservation and editing accuracy in local attribute editing.

Findings

Significantly improves editing fidelity and detail preservation.

Effectively aligns generated edits with textual attribute descriptions.

Outperforms existing methods in quantitative and qualitative evaluations.

Abstract

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional fine-tuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at https://github.com/connorxian/CA-Edit.