28 papers · 1 filter
ReDesign: Recovering Editable Design Structures from Images via Agentic Decomposition
Jooyeol Yun, Jintae Park, Hyesu Lim +3
Recovering an editable design file from a raster image is a common and costly bottleneck in modern design workflows, yet remains challenging since editability depends on recovering…
Probability-Conserving Flow Guidance
Parsa Esmati, Junha Hyung, Amirhossein Dadashzadeh +2
Diffusion and flow-based generative models dominate visual synthesis, with guidance aligning samples to user input and improving perceptual quality. However, Classifier-Free Guidan…
Learning to See What You Need: Gaze Attention for Multimodal Large Language Models
Junha Song, Byeongho Heo, Geonmo Gu +3
When humans describe a visual scene, they do not process the entire image uniformly; instead, they selectively fixate on regions relevant to their intended description. In contrast…
AHS: Adaptive Head Synthesis via Synthetic Data Augmentations
Taewoong Kang, Hyojin Jang, Sohyun Jeong +4
Recent digital media advancements have created increasing demands for sophisticated portrait manipulation techniques, particularly head swapping, where one's head is seamlessly int…
OPRO: Orthogonal Panel-Relative Operators for Panel-Aware In-Context Image Generation
Sanghyeon Lee, Minwoo Lee, Euijin Shin +3
We introduce a parameter-efficient adaptation method for panel-aware in-context image generation with pre-trained diffusion transformers. The key idea is to compose learnable, pane…
Memory-Efficient Fine-Tuning Diffusion Transformers via Dynamic Patch Sampling and Block Skipping
Sunghyun Park, Jeongho Kim, Hyoungwoo Park +6
Diffusion Transformers (DiTs) have significantly enhanced text-to-image (T2I) generation quality, enabling high-quality personalized content creation. However, fine-tuning these mo…