7 papers
CoCoIns: Consistent Subject Generation via Contrastive Instantiated Concepts
Lee Hsin-Ying, Kelvin C. K. Chan, Ming-Hsuan Yang
While text-to-image generative models can synthesize diverse and faithful content, subject variation across multiple generations limits their application to long-form content gener…
From Prompt to Progression: Taming Video Diffusion Models for Seamless Attribute Transition
Ling Lo, Kelvin C. K. Chan, Wen-Huang Cheng +1
Existing models often struggle with complex temporal changes, particularly when generating videos with gradual attribute transitions. The most common prompt interpolation approach…
KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entities
Hsin-Ping Huang, Xinyi Wang, Yonatan Bitton +8
Recent advances in text-to-image generation have improved the quality of synthesized images, but evaluations mainly focus on aesthetics or alignment with text prompts. Thus, it rem…
Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restoration
Xin Lin, Yuyan Zhou, Jingtong Yue +4
Unsupervised restoration approaches based on generative adversarial networks (GANs) offer a promising solution without requiring paired datasets. Yet, these GAN-based approaches st…
HoliGS: Holistic Gaussian Splatting for Embodied View Synthesis
Xiaoyuan Wang, Yizhou Zhao, Botao Ye +6
We propose HoliGS, a novel deformable Gaussian splatting framework that addresses embodied view synthesis from long monocular RGB videos. Unlike prior 4D Gaussian splatting and dyn…
A Simple Approach to Unifying Diffusion-based Conditional Generation
Xirui Li, Charles Herrmann, Kelvin C. K. Chan +4
Recent progress in image generation has sparked research into controlling these models through condition signals, with various methods addressing specific challenges in conditional…