DeepCalliFont: Few-Shot Chinese Calligraphy Font Synthesis by Integrating Dual-Modality Generative Models
Authors: Yitian Liu, Zhouhui Lian
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both qualitative and quantitative experiments have been conducted to demonstrate the superiority of our method to other state-of-the-art approaches in the task of few-shot Chinese calligraphy font synthesis. |
| Researcher Affiliation | Academia | Wangxuan Institute of Computer Technology, Peking University, Beijing, P.R. China {lsflyt, lianzhouhui}@pku.edu.cn |
| Pseudocode | No | The paper includes diagrams and mathematical formulas, but no structured pseudocode or algorithm blocks labeled as such. |
| Open Source Code | Yes | The source code can be found at https://github.com/lsflytpku/Deep Calli Font. |
| Open Datasets | Yes | We use 251 fonts and CASIA Online Chinese Handwriting Databases (Liu et al. 2011) to pre-train two branches separately in the pre-training phase 1. Then, in the pretraining phase 2, we selected 42 fonts used in (Jiang et al. 2019), each of which consists of 3,000 glyph images and their corresponding writing trajectories, to train the whole model. ... We use 30 fonts in the dataset collected by Liu and Lian (Liu and Lian 2023) as the regular font test set. |
| Dataset Splits | No | The paper states, 'we fine-tune networks on 100 sample characters and test them on other 6,663 Chinese characters.' While it defines training and testing data, it does not explicitly mention a separate validation set or its split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software details with version numbers (e.g., library names with versions like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | In this paper, θ and w are chosen as 100 and 2, respectively. |