Deciphering the oracle bone script plays a significant role in Chinese
archaeology and philology. However, it is significantly challenging due to the
scarcity of oracle character images. To overcome this issue, we propose
Diff-Oracle, based on diffusion models (DMs), to generate sufficient
controllable oracle characters. In contrast to most DMs that rely on text
prompts, we incorporate a style encoder to control style information during the
generation process. This encoder extracts style prompts from existing oracle
character images, where style details are converted from a CLIP model into a
text embedding format. Inspired by ControlNet, we introduce a content encoder
to capture desired content information from content images, ensuring the
fidelity of character glyphs. To train Diff-Oracle effectively, we propose to
obtain pixel-level paired oracle character images (i.e., style and content
images) by a pre-trained image-to-image translation model. Extensive
qualitative and quantitative experiments conducted on two benchmark datasets,
Oracle-241 and OBC306, demonstrate that our Diff-Oracle outperforms existing
generative methods in terms of image generation, further enhancing recognition
accuracy. Source codes will be available.

利用扩散模型生成足够可控的甲骨文字，通过样式编码器和内容编码器控制风格和内容信息，有效提高图像生成和识别准确性。

Diff-Oracle：具有可控风格和内容的 Oracle 字符生成的扩散模型

Diff-Oracle: Diffusion Model for Oracle Character Generation with  Controllable Styles and Contents

Neural style transfer has drawn broad attention in recent years. However,
most existing methods aim to explicitly model the transformation between
different styles, and the learned model is thus not generalizable to new
styles. We here attempt to separate the representations for styles and
contents, and propose a generalized style transfer network consisting of style
encoder, content encoder, mixer and decoder. The style encoder and content
encoder are used to extract the style and content factors from the style
reference images and content reference images, respectively. The mixer employs
a bilinear model to integrate the above two factors and finally feeds it into a
decoder to generate images with target style and content. To separate the style
features and content features, we leverage the conditional dependence of styles
and contents given an image. During training, the encoder network learns to
extract styles and contents from two sets of reference images in limited size,
one with shared style and the other with shared content. This learning
framework allows simultaneous style transfer among multiple styles and can be
deemed as a special `multi-task' learning scenario. The encoders are expected
to capture the underlying features for different styles and contents which is
generalizable to new styles and contents. For validation, we applied the
proposed algorithm to the Chinese Typeface transfer problem. Extensive
experiment results on character generation have demonstrated the effectiveness
and robustness of our method.

提出一种广义的神经风格转移网络，由风格解码器、内容解码器、混合器和解码器组成，可用于多种风格的图像转移任务。