We present novel approaches involving generative adversarial networks and
diffusion models in order to synthesize high quality, live and spoof
fingerprint images while preserving features such as uniqueness and diversity.
We generate live fingerprints from noise with a variety of methods, and we use
image translation techniques to translate live fingerprint images to spoof. To
generate different types of spoof images based on limited training data we
incorporate style transfer techniques through a cycle autoencoder equipped with
a Wasserstein metric along with Gradient Penalty (CycleWGAN-GP) in order to
avoid mode collapse and instability. We find that when the spoof training data
includes distinct spoof characteristics, it leads to improved live-to-spoof
translation. We assess the diversity and realism of the generated live
fingerprint images mainly through the Fr\'echet Inception Distance (FID) and
the False Acceptance Rate (FAR). Our best diffusion model achieved a FID of
15.78. The comparable WGAN-GP model achieved slightly higher FID while
performing better in the uniqueness assessment due to a slightly lower FAR when
matched against the training data, indicating better creativity. Moreover, we
give example images showing that a DDPM model clearly can generate realistic
fingerprint images.

利用生成对抗网络和扩散模型，我们提出了新颖的方法来合成高质量、真实和仿冒指纹图像，同时保留独特性和多样性特征。通过多种方法从噪声生成真实的指纹图像，并利用图像转换技术将真实的指纹图像转换为仿冒的指纹图像。通过循环自动编码器和梯度惩罚加权里程碑（CycleWGAN-GP）等风格转换技术，结合 Wasserstein 距离指标，我们实现了基于有限训练数据生成不同类型的仿冒图像，以避免模式崩溃和不稳定性。我们发现，当仿冒训练数据包含明显的仿冒特征时，可以改善从真实到仿冒的转换。我们主要通过 Fréchet Inception Distance（FID）和 False Acceptance Rate（FAR）评估生成的真实指纹图像的多样性和逼真性。我们最佳的扩散模型达到了 15.78 的 FID。而相比之下，WGAN-GP 模型在独特性评估方面表现更好，因其在与训练数据匹配时的稍低 FAR，表明更好的创造力。此外，我们给出了示例图像，清楚地显示了 DDPM 模型能够生成逼真的指纹图像。

利用 GAN、扩散模型和风格迁移技术增强指纹图像合成

Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and  Style Transfer Techniques

Recently, the multimedia community has witnessed the rise of diffusion models
trained on large-scale multi-modal data for visual content creation,
particularly in the field of text-to-image generation. In this paper, we
propose a new task for ``stylizing'' text-to-image models, namely text-driven
stylized image generation, that further enhances editability in content
creation. Given input text prompt and style image, this task aims to produce
stylized images which are both semantically relevant to input text prompt and
meanwhile aligned with the style image in style. To achieve this, we present a
new diffusion model (ControlStyle) via upgrading a pre-trained text-to-image
model with a trainable modulation network enabling more conditions of text
prompts and style images. Moreover, diffusion style and content regularizations
are simultaneously introduced to facilitate the learning of this modulation
network with these diffusion priors, pursuing high-quality stylized
text-to-image generation. Extensive experiments demonstrate the effectiveness
of our ControlStyle in producing more visually pleasing and artistic results,
surpassing a simple combination of text-to-image model and conventional style
transfer techniques.

提出了一种新的任务，文本驱动的风格化图像生成，以进一步增强内容创造中的可编辑性，通过升级经过训练的文本到图像模型与可训练的调制网络，同时引入扩散样式和内容正则化，实现了高质量的风格化文本到图像生成。

控制样式：使用扩散先验生成基于文本的风格化图像

ControlStyle: Text-Driven Stylized Image Generation Using Diffusion  Priors

Robotic painting has been a subject of interest among both artists and
roboticists since the 1970s. Researchers and interdisciplinary artists have
employed various painting techniques and human-robot collaboration models to
create visual mediums on canvas. One of the challenges of robotic painting is
to apply a desired artistic style to the painting. Style transfer techniques
with machine learning models have helped us address this challenge with the
visual style of a specific painting. However, other manual elements of style,
i.e., painting techniques and brushstrokes of an artist, have not been fully
addressed. We propose a method to integrate an artistic style to the
brushstrokes and the painting process through collaboration with a human
artist. In this paper, we describe our approach to 1) collect brushstrokes and
hand-brush motion samples from an artist, and 2) train a generative model to
generate brushstrokes that pertains to the artist's style, and 3) fine tune a
stroke-based rendering model to work with our robotic painting setup. We will
report on the integration of these three steps in a separate publication. In a
preliminary study, 71% of human evaluators find our reconstructed brushstrokes
are pertaining to the characteristics of the artist's style. Moreover, 58% of
participants could not distinguish a painting made by our method from a
visually similar painting created by a human artist.

该论文提出了一种结合机器和人类艺术家的方法，通过生成模型和渲染模型，将艺术家的风格融入机器人绘画中，并证实了该方法的可行性和有效性。