6 papers
Accelerated Likelihood Maximization for Diffusion-based Versatile Content Generation
Hyunsoo Lee, Inwoo Hwang, Young Min Kim
Generating diverse, coherent, and plausible content from partially given inputs remains a fundamental challenge for diffusion models. Existing approaches face clear limitations: tr…
Calibrated Test-Time Guidance for Bayesian Inference
Daniel Geyfman, Felix Draxler, Jan Groeneveld +3
Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function. Existing approaches, however, focus on maximi…
Variational Test-time Optimization for Diffusion Synchronization
Hyunsoo Lee, Farrin Marouf Sofian, Kushagra Pandey +1
Collaborative generation, which coordinates multiple diffusion trajectories to extend the capabilities of pretrained priors, has emerged as a powerful paradigm for extending the ap…
Low-Resolution Editing is All You Need for High-Resolution Editing
Junsung Lee, Hyunsoo Lee, Yong Jae Lee +1
High-resolution content creation is rapidly emerging as a central challenge in both the vision and graphics communities. Images serve as the most fundamental modality for visual ex…
Image-Guided Geometric Stylization of 3D Meshes
Changwoon Choi, Hyunsoo Lee, Clément Jambon +2
Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control signals, such as contextual…
Diffusion-Based Conditional Image Editing through Optimized Inference with Guidance
Hyunsoo Lee, Minsoo Kang, Bohyung Han
We present a simple but effective training-free approach for text-driven image-to-image translation based on a pretrained text-to-image diffusion model. Our goal is to generate an…