12 citations · 15 across the 14 of their papers we have counts for
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RADIANCE: Relative Adaptive Denoising with IP-Adapter for Novel Concept Enhancement
Zi-Xiang Ni, Bo-Lun Huang, Teng-Fang Hsiao +2
Text-to-image (T2I) diffusion models have achieved striking progress but still struggle to synthesize rare concepts involving unusual attribute-object pairings, often resulting in…
VecSet-Edit: Unleashing Pre-trained LRM for Mesh Editing from Single Image
Teng-Fang Hsiao, Bo-Kai Ruan, Yu-Lun Liu +1
3D editing has emerged as a critical research area to provide users with flexible control over 3D assets. While current editing approaches predominantly focus on 3D Gaussian Splatt…
Diversifying Long Prompt Image Generation through Structured Prompt Embedding Space Sampling
Bo-Kai Ruan, Teng-Fang Hsiao, Ling Lo +2
Modern text-to-image models produce impressive visual results from richly specified prompts, yet their behavior under long prompts remains insufficiently understood. In this paper,…
Ranking-based Preference Optimization for Diffusion Models from Implicit User Feedback
Yi-Lun Wu, Bo-Kai Ruan, Chiang Tseng +1
Direct preference optimization (DPO) methods have shown strong potential in aligning text-to-image diffusion models with human preferences by training on paired comparisons. These…
Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings for Improved Diffusion Generation
Sung-Lin Tsai, Bo-Lun Huang, Yu Ting Shen +5
Accurate color alignment in text-to-image (T2I) generation is critical for applications such as fashion, product visualization, and interior design, yet current diffusion models st…
Adversarial Attacks on VQA-NLE: Exposing and Alleviating Inconsistencies in Visual Question Answering Explanations
Yahsin Yeh, Yilun Wu, Bokai Ruan +1
Natural language explanations in visual question answering (VQA-NLE) aim to make black-box models more transparent by elucidating their decision-making processes. However, we find…
Score Replacement with Bounded Deviation for Rare Prompt Generation
Bo-Kai Ruan, Zi-Xiang Ni, Bo-Lun Huang +2
Diffusion models achieve impressive performance in high-fidelity image generation but often struggle with rare concepts that appear infrequently in the training distribution. Prior…
MAD: Makeup All-in-One with Cross-Domain Diffusion Model
Bo-Kai Ruan, Hong-Han Shuai
Existing makeup techniques often require designing multiple models to handle different inputs and align features across domains for different makeup tasks, e.g., beauty filter, mak…
TF-TI2I: Training-Free Text-and-Image-to-Image Generation via Multi-Modal Implicit-Context Learning in Text-to-Image Models
Teng-Fang Hsiao, Bo-Kai Ruan, Yi-Lun Wu +2
Text-and-Image-To-Image (TI2I), an extension of Text-To-Image (T2I), integrates image inputs with textual instructions to enhance image generation. Existing methods often partially…
FreeCond: Free Lunch in the Input Conditions of Text-Guided Inpainting
Teng-Fang Hsiao, Bo-Kai Ruan, Sung-Lin Tsai +2
In this study, we aim to determine and solve the deficiency of Stable Diffusion Inpainting (SDI) in following the instruction of both prompt and mask. Due to the training bias from…
Training-and-Prompt-Free General Painterly Harmonization via Zero-Shot Disentenglement on Style and Content References
Teng-Fang Hsiao, Bo-Kai Ruan, Hong-Han Shuai
Painterly image harmonization aims at seamlessly blending disparate visual elements within a single image. However, previous approaches often struggle due to limitations in trainin…
Vision Transformers: State of the Art and Research Challenges
Bo-Kai Ruan, Hong-Han Shuai, Wen-Huang Cheng
Transformers have achieved great success in natural language processing. Due to the powerful capability of self-attention mechanism in transformers, researchers develop the vision…