3 citations · 6 across the 9 of their papers we have counts for
9 papers · 1 filter
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…
Training Spatial-Frequency Visual Prompts and Probabilistic Clusters for Accurate Black-Box Transfer Learning
Wonwoo Cho, Kangyeol Kim, Saemee Choi +1
Despite the growing prevalence of black-box pre-trained models (PTMs) such as prediction API services, there remains a significant challenge in directly applying general models to…
Layout-and-Retouch: A Dual-stage Framework for Improving Diversity in Personalized Image Generation
Kangyeol Kim, Wooseok Seo, Sehyun Nam +5
Personalized text-to-image (P-T2I) generation aims to create new, text-guided images featuring the personalized subject with a few reference images. However, balancing the trade-of…
iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds
Dongmin Choi, Wonwoo Cho, Kangyeol Kim +1
Accurately annotating multiple 3D objects in LiDAR scenes is laborious and challenging. While a few previous studies have attempted to leverage semi-automatic methods for cost-effe…
Reference-based Image Composition with Sketch via Structure-aware Diffusion Model
Kangyeol Kim, Sunghyun Park, Junsoo Lee +1
Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images. To further enhance editability and en…
Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment
Taewoo Kim, Chaeyeon Chung, Yoonseo Kim +3
Editing hairstyle is unique and challenging due to the complexity and delicacy of hairstyle. Although recent approaches significantly improved the hair details, these models often…
Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE
Kangyeol Kim, Sunghyun Park, Junsoo Lee +4
In order to perform unconditional video generation, we must learn the distribution of the real-world videos. In an effort to synthesize high-quality videos, various studies attempt…
Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation
Sunghyun Park, Kangyeol Kim, Junsoo Lee +4
Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e.g., increas…
Unpaired Image Translation via Adaptive Convolution-based Normalization
Wonwoong Cho, Kangyeol Kim, Eungyeup Kim +2
Disentangling content and style information of an image has played an important role in recent success in image translation. In this setting, how to inject given style into an inpu…