SCA-Font: Enhancing Few-Shot Generation with Style-Content Aggregation
Yefei Wang, Sunzhe Yang, Kangyue Xiong, Jinshan Zeng · 2024
The few-shot font generation (FFG) task aims to create a new font library using only a small number of reference samples. The predominated methods for this task are mainly based on the style-content disentangled representation learning. Existing style-content disentangling based few-shot font generation models are mainly devoted to the extraction of better content and style features by leveraging extra prior information such as strokes and skeletons, or introducing auxiliary networks while ignoring the aggregation scheme of style and content features. To address this issue, we propose a novel few-shot font generation method called SCA-Font by introducing an effective style-content feature aggregation module (SCAM), where the content features from the source characters and the style features from the target reference characters are effectively aggregated by a novel neural network. Experimental results on a dataset of 35 font styles collected by ourselves demonstrate that the proposed SCA-Font model outperforms state-of-the-art models in both quantitative results and the quality of generated characters. We also verify the effect of the number of shots for the proposed model. Numerical experiment results show that six shots of reference characters are preferred to achieve the best performance of the proposed model.