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DARE to Mitigate Hallucination: Dual-path Auto-Regressive-aware Editing
Jae-Ho Lee, Jeong-Eun Lee, Gyeong-Moon Park
Large vision-language models (LVLMs) have recently achieved remarkable progress across multimodal tasks, yet object hallucination remains a persistent challenge where models genera…
OSVE: One Step Video Editing with One Step Diffusion Models
Habin Lim, Gyeong-Moon Park
Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion. We present OSVE, the first framework to successfully ad…
Perturb a Model, Not an Image: Towards Robust Privacy Protection via Anti-Personalized Diffusion Models
Tae-Young Lee, Juwon Seo, Jong Hwan Ko +1
Recent advances in diffusion models have enabled high-quality synthesis of specific subjects, such as identities or objects. This capability, while unlocking new possibilities in c…
EPSilon: Efficient Point Sampling for Lightening of Hybrid-based 3D Avatar Generation
Seungjun Moon, Sangjoon Yu, Gyeong-Moon Park
The rapid advancement of neural radiance fields (NeRF) has paved the way to generate animatable human avatars from a monocular video. However, the sole usage of NeRF suffers from a…
Generative Unlearning for Any Identity
Juwon Seo, Sung-Hoon Lee, Tae-Young Lee +2
Recent advances in generative models trained on large-scale datasets have made it possible to synthesize high-quality samples across various domains. Moreover, the emergence of str…
IntereStyle: Encoding an Interest Region for Robust StyleGAN Inversion
Seungjun Moon, Gyeong-Moon Park
Recently, manipulation of real-world images has been highly elaborated along with the development of Generative Adversarial Networks (GANs) and corresponding encoders, which embed…