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Font Generation

Papers

Showing 125 of 52 papers

TitleStatusHype
FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive LearningCode2
DGFont++: Robust Deformable Generative Networks for Unsupervised Font GenerationCode2
MX-Font++: Mixture of Heterogeneous Aggregation Experts for Few-shot Font GenerationCode1
Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion ModelsCode1
DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative ModelsCode1
Few shot font generation via transferring similarity guided global style and quantization local styleCode1
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and QuantizationCode1
CF-Font: Content Fusion for Few-shot Font GenerationCode1
Neural Transformation Fields for Arbitrary-Styled Font GenerationCode1
Diff-Font: Diffusion Model for Robust One-Shot Font GenerationCode1
GAS-NeXt: Few-Shot Cross-Lingual Font GeneratorCode1
Instance Segmentation for Chinese Character Stroke Extraction, Datasets and BenchmarksCode1
Few-Shot Font Generation by Learning Fine-Grained Local StylesCode1
Look Closer to Supervise Better: One-Shot Font Generation via Component-Based DiscriminatorCode1
Few-shot Font Generation with Weakly Supervised Localized RepresentationsCode1
DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality LearningCode1
Font Style that Fits an Image -- Font Generation Based on Image ContextCode1
DG-Font: Deformable Generative Networks for Unsupervised Font GenerationCode1
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized ExpertsCode1
StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke EncodingCode1
Few-shot Font Generation with Localized Style Representations and FactorizationCode1
Few-shot Compositional Font Generation with Dual MemoryCode1
Skeleton and Font Generation Network for Zero-shot Chinese Character Generation0
Font-Agent: Enhancing Font Understanding with Large Language Models0
One-Shot Multilingual Font Generation Via ViT0
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