6 papers · 1 filter
TextGuider: Training-Free Guidance for Text Rendering via Attention Alignment
Kanghyun Baek, Sangyub Lee, Jin Young Choi +6
Despite recent advances, diffusion-based text-to-image models still struggle with accurate text rendering. Several studies have proposed fine-tuning or training-free refinement met…
DCText: Scheduled Attention Masking for Visual Text Generation via Divide-and-Conquer Strategy
Jaewoo Song, Jooyoung Choi, Kanghyun Baek +3
Despite recent text-to-image models achieving highfidelity text rendering, they still struggle with long or multiple texts due to diluted global attention. We propose DCText, a tra…
Negative-Guided Subject Fidelity Optimization for Zero-Shot Subject-Driven Generation
Chaehun Shin, Jooyoung Choi, Johan Barthelemy +2
We present Subject Fidelity Optimization (SFO), a novel comparative learning framework for zero-shot subject-driven generation that enhances subject fidelity. Existing supervised f…
Improving Geometry in Sparse-View 3DGS via Reprojection-based DoF Separation
Yongsung Kim, Minjun Park, Jooyoung Choi +1
Recent learning-based Multi-View Stereo models have demonstrated state-of-the-art performance in sparse-view 3D reconstruction. However, directly applying 3D Gaussian Splatting (3D…
Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator
Chaehun Shin, Jooyoung Choi, Heeseung Kim +1
Subject-driven text-to-image generation aims to produce images of a new subject within a desired context by accurately capturing both the visual characteristics of the subject and…
Style-Friendly SNR Sampler for Style-Driven Generation
Jooyoung Choi, Chaehun Shin, Yeongtak Oh +3
Recent text-to-image diffusion models generate high-quality images but struggle to learn new, personalized styles, which limits the creation of unique style templates. In style-dri…