1 citations · 2 across the 8 of their papers we have counts for
8 papers
UniSAFE: A Comprehensive Benchmark for Safety Evaluation of Unified Multimodal Models
Segyu Lee, Boryeong Cho, Hojung Jung +8
Unified Multimodal Models (UMMs) offer powerful cross-modality capabilities but introduce new safety risks not observed in single-task models. Despite their emergence, existing saf…
Self-Refining Video Sampling
Sangwon Jang, Taekyung Ki, Jaehyeong Jo +3
Modern video generators still struggle with complex physical dynamics, often falling short of physical realism. Existing approaches address this using external verifiers or additio…
Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
Taekyung Ki, Sangwon Jang, Jaehyeong Jo +2
Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation. However, current models do not yet convey the feeling of trul…
Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models
Sangwon Jang, Taekyung Ki, Jaehyeong Jo +4
Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tunin…
Enhancing Variational Autoencoders with Smooth Robust Latent Encoding
Hyomin Lee, Minseon Kim, Sangwon Jang +2
Variational Autoencoders (VAEs) have played a key role in scaling up diffusion-based generative models, as in Stable Diffusion, yet questions regarding their robustness remain larg…
Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models
Sangwon Jang, June Suk Choi, Jaehyeong Jo +2
Text-to-image diffusion models have achieved remarkable success in generating high-quality contents from text prompts. However, their reliance on publicly available data and the gr…
Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models
Sangwon Jang, Jaehyeong Jo, Kimin Lee +1
Text-to-image diffusion models have shown remarkable success in generating personalized subjects based on a few reference images. However, current methods often fail when generatin…
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
Jaewoong Lee, Sangwon Jang, Jaehyeong Jo +5
Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding. While recent token-based approaches achieve competitive performanc…