8 papers · 1 filter
Layer-Aware Video Composition via Split-then-Merge
Ozgur Kara, Yujia Chen, Ming-Hsuan Yang +3
We present Split-then-Merge (StM), a novel framework designed to enhance control in generative video composition and address its data scarcity problem. Unlike conventional methods…
Scaling Laws for Deepfake Detection
Wenhao Wang, Longqi Cai, Jusheng Zhang +3
This paper presents a systematic study of scaling laws for the deepfake detection task. Specifically, we analyze the model performance against the number of real image domains, dee…
SceneAdapt: Scene-aware Adaptation of Human Motion Diffusion
Jungbin Cho, Minsu Kim, Jisoo Kim +5
Human motion is inherently diverse and semantically rich, while also shaped by the surrounding scene. However, existing motion generation approaches fail to generate semantically d…
From Prompt to Progression: Taming Video Diffusion Models for Seamless Attribute Transition
Ling Lo, Kelvin C. K. Chan, Wen-Huang Cheng +1
Existing models often struggle with complex temporal changes, particularly when generating videos with gradual attribute transitions. The most common prompt interpolation approach…
Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detection
Fangling Jiang, Qi Li, Bing Liu +4
3D mask presentation attack detection is crucial for protecting face recognition systems against the rising threat of 3D mask attacks. While most existing methods utilize multimoda…
CompleteMe: Reference-based Human Image Completion
Yu-Ju Tsai, Brian Price, Qing Liu +5
Recent methods for human image completion can reconstruct plausible body shapes but often fail to preserve unique details, such as specific clothing patterns or distinctive accesso…
Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model
Qi Mao, Lan Chen, Yuchao Gu +2
Balancing fidelity and editability is essential in text-based image editing (TIE), where failures commonly lead to over- or under-editing issues. Existing methods typically rely on…
CoCoIns: Consistent Subject Generation via Contrastive Instantiated Concepts
Lee Hsin-Ying, Kelvin C. K. Chan, Ming-Hsuan Yang
While text-to-image generative models can synthesize diverse and faithful content, subject variation across multiple generations limits their application to long-form content gener…