43 citations · 89 across the 14 of their papers we have counts for
14 papers
StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models
Zhizhong Wang, Lei Zhao, Wei Xing
Content and style (C-S) disentanglement is a fundamental problem and critical challenge of style transfer. Existing approaches based on explicit definitions (e.g., Gram matrix) or…
VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs
Jiakai Sun, Zhanjie Zhang, Jiafu Chen +5
Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to…
Joint Optimization of Triangle Mesh, Material, and Light from Neural Fields with Neural Radiance Cache
Jiakai Sun, Weijing Zhang, Zhanjie Zhang +4
Traditional inverse rendering techniques are based on textured meshes, which naturally adapts to modern graphics pipelines, but costly differentiable multi-bounce Monte Carlo (MC)…
Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning
Zhiwen Zuo, Lei Zhao, Ailin Li +5
This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an advers…
MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer
Zhizhong Wang, Lei Zhao, Zhiwen Zuo +4
Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to…
AesUST: Towards Aesthetic-Enhanced Universal Style Transfer
Zhizhong Wang, Zhanjie Zhang, Lei Zhao +4
Recent studies have shown remarkable success in universal style transfer which transfers arbitrary visual styles to content images. However, existing approaches suffer from the aes…