TFGNet: Target Face Generation from Low-Quality Images via Textual Guidance

Recent advances indicate that text-guided face generation has attracted considerable attention in the field of computer vision. However, most existing methods assume high-quality (HQ) face inputs, while how to generate HQ and identity-preserving faces from low-quality (LQ) images is still an open problem. In this paper, we propose a novel face generation approach, termed TFGNet, which synthesizes HQ face images from LQ inputs guided by textual descriptions. Unlike most existing methods that rely on HQ inputs, TFGNet leverages external textual descriptions as semantic guidance to directly generate HQ and identity-preserving faces from degraded images. First, we design a unified framework that integrates a Transformer-based encoder, a codebook mechanism, and multimodal representations extracted from contrastive language-image pretraining (CLIP) model to produce enhanced cross-modal embeddings, which are then decoded by a diffusion model to generate target face images with both high visual fidelity and accurate identity (ID) preservation. Second, we propose a masked diffusion loss that emphasizes identity-related regions and embed it into a dynamically weighted total loss, enabling a balanced trade-off among visual fidelity, semantic coherence, and ID consistency. Third, we construct a multimodal dataset comprising LQ face images, HQ targets, and manually annotated textual descriptions to address the scarcity of suitable text-image pairs for this task. Extensive experimental results demonstrate that the proposed TFGNet approach outperforms many state-of-the-art face generation methods in terms of both objective metrics and perceptual quality. The source code of this work is available at: https://github.com/wenxin-cv/TFGNet.