Content and style (CS) disentanglement is a fundamental problem and critical challenge of
style transfer. Existing approaches based on explicit definitions (eg, Gram matrix) or implicit …

Human parsing aims to partition humans in image or video into multiple pixel-level semantic
parts. In the last decade, it has gained significantly increased interest in the computer vision …

State-of-the-art parametric and non-parametric style transfer approaches are prone to either
distorted local style patterns due to global statistics alignment, or unpleasing artifacts …

One key challenge of exemplar-guided image generation lies in establishing fine-grained
correspondences between input and guided images. Prior approaches, despite the …

Arbitrary style transfer has been demonstrated to be efficient in artistic image generation.
Previous methods either globally modulate the content feature ignoring local details, or …

The field of neural style transfer has experienced a surge of research exploring different
avenues ranging from optimization-based approaches and feed-forward models to meta …

Transformer-based models achieve favorable performance in artistic style transfer recently
thanks to its global receptive field and powerful multi-head/layer attention operations …

Recent advances in 3D scene representation and novel view synthesis have witnessed the
rise of Neural Radiance Fields (NeRFs). Nevertheless, it is not trivial to exploit NeRF for the …

Image harmonization aims to produce visually harmonious composite images by adjusting
the foreground appearance to be compatible with the background. When the composite …

Artistic style transfer aims to create new artistic images by rendering a given photograph with
the target artistic style. Existing methods learn styles simply based on global statistics or …