To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

We present a method for transferring the artistic features of an arbitrary style image to a 3D
scene. Previous methods that perform 3D stylization on point clouds or meshes are sensitive …

Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging
visual learning tasks, which can be cast as a feature distribution matching problem. With the …

In image-to-image translation, each patch in the output should reflect the content of the
corresponding patch in the input, independent of domain. We propose a straightforward …

Plain text has become a prevalent interface for text-to-image synthesis. However, its limited
customization options hinder users from accurately describing desired outputs. For example …

Generative adversarial networks (GANs) have recently found applications in image editing.
However, most GAN-based image editing methods often require large-scale datasets with …

Deep generative models have become increasingly effective at producing realistic images
from randomly sampled seeds, but using such models for controllable manipulation of …

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 …

Deep learning has substantially boosted the performance of Monocular Depth Estimation
(MDE), a critical component in fully vision-based autonomous driving (AD) systems (eg …