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Found 17 papers, 7 papers with code
PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping
3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene.
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation
Given that many deterministic conditional image generative models have been able to produce high-quality yet fixed results, we raise an intriguing question: is it possible for pre-trained deterministic conditional image generative models to generate diverse results without changing network structures or parameters?
StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models
Our work provides new insights into the C-S disentanglement in style transfer and demonstrates the potential of diffusion models for learning well-disentangled C-S characteristics.
Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning
By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes.
Rethinking Fast Fourier Convolution in Image Inpainting
Recently proposed image inpainting method LaMa builds its network upon Fast Fourier Convolution (FFC), which was originally proposed for high-level vision tasks like image classification.
MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer
The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations.
AesUST: Towards Aesthetic-Enhanced Universal Style Transfer
Specifically, our approach introduces an aesthetic discriminator to learn the universal human-delightful aesthetic features from a large corpus of artist-created paintings.
Texture Reformer: Towards Fast and Universal Interactive Texture Transfer
In addition, we also introduce a novel learning-free view-specific texture reformation (VSTR) operation with a new semantic map guidance strategy to achieve more accurate semantic-guided and structure-preserved texture transfer.
Artistic Style Transfer with Internal-external Learning and Contrastive Learning
Although existing artistic style transfer methods have achieved significant improvement with deep neural networks, they still suffer from artifacts such as disharmonious colors and repetitive patterns.
DualAST: Dual Style-Learning Networks for Artistic Style Transfer
Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles.
DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer
Gram-based and patch-based approaches are two important research lines of style transfer.
Diverse Image Style Transfer via Invertible Cross-Space Mapping
Image style transfer aims to transfer the styles of artworks onto arbitrary photographs to create novel artistic images.
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization
Multimodal image-to-image translation (I2IT) aims to learn a conditional distribution that explores multiple possible images in the target domain given an input image in the source domain.
LDMGAN: Reducing Mode Collapse in GANs with Latent Distribution Matching
Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images.
Cascade Style Transfer
This could help improve the quality and flexibility, and guide us to find domain-independent approaches.
Diversified Arbitrary Style Transfer via Deep Feature Perturbation
Image style transfer is an underdetermined problem, where a large number of solutions can satisfy the same constraint (the content and style).
GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features
Our approach is not only flexible to adjust the trade-off between content and style, but also controllable between global and local.
