Face restoration has achieved remarkable advancements through the years of development.
However, ensuring that restored facial images exhibit high fidelity, preserve authentic
features, and avoid introducing artifacts or biases remains a significant challenge.
This highlights the need for models that are more "honest" in their reconstruction from
low-quality inputs, accurately reflecting original characteristics.
In this work, we propose HonestFace, a novel approach designed to restore faces with
a strong emphasis on such honesty, particularly concerning identity consistency and
texture realism. To achieve this, HonestFace incorporates several key components.
First, we propose an identity embedder to effectively capture and preserve crucial
identity features from both the low-quality input and multiple reference faces.
Second, a masked face alignment method is presented to enhance fine-grained details
and textural authenticity, thereby preventing the generation of patterned or overly
synthetic textures and improving overall clarity.
Furthermore, we present a new landmark-based evaluation metric. Based on affine
transformation principles, this metric improves the accuracy compared to conventional
L2 distance calculations for facial feature alignment. Leveraging these contributions
within a one-step diffusion model framework, HonestFace delivers exceptional
restoration results in terms of facial fidelity and realism. Extensive experiments
demonstrate that our approach surpasses existing methods, achieving
superior performance in both visual quality and quantitative assessments.
