43 citations · 54 across the 10 of their papers we have counts for
10 papers
Dynamic Image Prompt Adapter for Scalable Zero-shot Personalized Text-to-Image Generation
Zhizhong Wang, Tianyi Chu, Zeyi Huang +2
Personalized Text-to-Image (PT2I) generation aims to produce customized images based on reference images. A prominent interest pertains to the integration of an image prompt adapte…
BideDPO: Conditional Image Generation with Simultaneous Text and Condition Alignment
Dewei Zhou, Mingwei Li, Zongxin Yang +5
Conditional image generation enhances text-to-image synthesis with structural, spatial, or stylistic priors, but current methods face challenges in handling conflicts between sourc…
MultiCrafter: High-Fidelity Multi-Subject Generation via Disentangled Attention and Identity-Aware Preference Alignment
Tao Wu, Yibo Jiang, Yehao Lu +4
Multi-subject image generation aims to synthesize user-provided subjects in a single image while preserving subject fidelity, ensuring prompt consistency, and aligning with human a…
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation
Tianyi Chu, Wei Xing, Jiafu Chen +5
Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for hig…
PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping
Jiafu Chen, Wei Xing, Jiakai Sun +7
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 a…
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…
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…
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…
Texture Reformer: Towards Fast and Universal Interactive Texture Transfer
Zhizhong Wang, Lei Zhao, Haibo Chen +4
In this paper, we present the texture reformer, a fast and universal neural-based framework for interactive texture transfer with user-specified guidance. The challenges lie in thr…
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization
Zhiwen Zuo, Lei Zhao, Zhizhong Wang +5
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…