  Facial attribute editing plays a crucial role in synthesizing realistic faces
with specific characteristics while maintaining realistic appearances. Despite
advancements, challenges persist in achieving precise, 3D-aware attribute
modifications, which are crucial for consistent and accurate representations of
faces from different angles. Current methods struggle with semantic
entanglement and lack effective guidance for incorporating attributes while
maintaining image integrity. To address these issues, we introduce a novel
framework that merges the strengths of latent-based and reference-based editing
methods. Our approach employs a 3D GAN inversion technique to embed attributes
from the reference image into a tri-plane space, ensuring 3D consistency and
realistic viewing from multiple perspectives. We utilize blending techniques
and predicted semantic masks to locate precise edit regions, merging them with
the contextual guidance from the reference image. A coarse-to-fine inpainting
strategy is then applied to preserve the integrity of untargeted areas,
significantly enhancing realism. Our evaluations demonstrate superior
performance across diverse editing tasks, validating our framework's
effectiveness in realistic and applicable facial attribute editing.
