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Found 10 papers, 4 papers with code
DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models
Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain.
Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations
Due to the data absence, the textual description of the target domain and the vision-language models, e. g., CLIP, are utilized to effectively guide the generator.
AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks
To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image.
DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models
In this study, we aim to extend the capabilities of diffusion-based text-to-image (T2I) generation models by incorporating diverse modalities beyond textual description, such as sketch, box, color palette, and style embedding, within a single model.
Interactive Cartoonization with Controllable Perceptual Factors
In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures.
Exploiting Domain Transferability for Collaborative Inter-level Domain Adaptive Object Detection
Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations.
Feature Stylization and Domain-aware Contrastive Learning for Domain Generalization
To these ends, we propose a novel domain generalization framework where feature statistics are utilized for stylizing original features to ones with novel domain properties.
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Domain-Aware Universal Style Transfer
To this end, we design a novel domainness indicator that captures the domainness value from the texture and structural features of reference images.
Continuous Face Aging Generative Adversarial Networks
Face aging is the task aiming to translate the faces in input images to designated ages.
ArrowGAN : Learning to Generate Videos by Learning Arrow of Time
Training GANs on videos is even more sophisticated than on images because videos have a distinguished dimension: time.
