论文标题
笔迹生成的扩散模型
Diffusion models for Handwriting Generation
论文作者
论文摘要
在本文中，我们提出了一种用于手写生成的扩散概率模型。扩散模型是一类生成模型，在该模型中，样品从高斯噪声开始，并逐渐被降低以产生输出。我们的手写生成方法不需要使用任何基于文本识别的，基于作者风格或对抗性损失功能的方法，也不需要培训辅助网络。我们的模型能够直接从图像数据中直接合并作者风格功能，从而消除了在采样过程中对用户互动的需求。实验表明，我们的模型能够以与给定作者相似的样式生成逼真的，高质量的手写文本图像。我们的实施可以在https://github.com/tcl9876/diffusion frankwriting-generation上找到
In this paper, we propose a diffusion probabilistic model for handwriting generation. Diffusion models are a class of generative models where samples start from Gaussian noise and are gradually denoised to produce output. Our method of handwriting generation does not require using any text-recognition based, writer-style based, or adversarial loss functions, nor does it require training of auxiliary networks. Our model is able to incorporate writer stylistic features directly from image data, eliminating the need for user interaction during sampling. Experiments reveal that our model is able to generate realistic , high quality images of handwritten text in a similar style to a given writer. Our implementation can be found at https://github.com/tcl9876/Diffusion-Handwriting-Generation
