Zero-shot Image generation and manipulation Few-shot的图像生成通俗讲就是指在目标域数据很有限的情况下,要实现目标域的图像生成。一般有两种主流的方法,一种是从头训练一个生成器,这种情况下,一般需要几百到几千张数据,一般会使用数据增广或者设计一些辅助任务来从现有数据更好得训练鉴别器。另一种方法是将预训练...
To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models. Based on full inversion capability and high-quality image generation power of recent diffusion models, our ...
Tedigan: Text-guided diverse face image generation and manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2256–2265, 2021. [63] Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. Bisenet: Bilateral segmentation...
Text and Image Guided 3D Avatar Generation and Manipulation Zehranaz Canfes, M. Atasoy, Alara Dirik, Pinar Yanardag 2022 HeadSculpt: Crafting 3D Head Avatars with Text Xiaoping Han, Yukang Cao, K. Han, Xiatian Zhu, Jiankang Deng, Yi-Zhe Song, Tao Xiang...
inversion capability and high-quality image generation power of recent diffusion models, our method performs zero-shot image manipulation successfully even between unseen domains and takes another step towards general application by manipulating images from a widely varying ImageNet dataset. Furthermore, ...
To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models. Based on full inversion capability and high-quality image generation power of recent diffusion models, our ...