7 papers · 1 filter
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior
Li-Yuan Tsao, Hao-Wei Chen, Hao-Wei Chung +4
Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR). However, existing methods may produce unintended results due to noi…
PocoLoco: A Point Cloud Diffusion Model of Human Shape in Loose Clothing
Siddharth Seth, Rishabh Dabral, Diogo Luvizon +4
Modeling a human avatar that can plausibly deform to articulations is an active area of research. We present PocoLoco -- the first template-free, point-based, pose-conditioned gene…
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…
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…
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…
Improving Subject-Driven Image Synthesis with Subject-Agnostic Guidance
Kelvin C. K. Chan, Yang Zhao, Xuhui Jia +2
In subject-driven text-to-image synthesis, the synthesis process tends to be heavily influenced by the reference images provided by users, often overlooking crucial attributes deta…
AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks with Adapters
Hao-Wei Chen, Yu-Syuan Xu, Kelvin C. K. Chan +3
Existing image restoration approaches typically employ extensive networks specifically trained for designated degradations. Despite being effective, such methods inevitably entail…