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cs.CV2026
Custom Forcing: Training-Free Subject Customization for Autoregressive Video Generation
Yunseung Ok, Hyunsoo Kim, Minseo Kim +1
Autoregressive video models can generate minute-long videos in real time, but they produce generic subjects from text rather than specific subjects from user-provided images. Exist…
cs.CV2026
ConceptPrism: Concept Disentanglement in Personalized Diffusion Models via Residual Token Optimization
Minseo Kim, Minchan Kwon, Dongyeun Lee +2
Personalized text-to-image (T2I) generation has emerged as a key application for creating user-specific concepts from a few reference images. The core challenge is concept disentan…
cs.CV2024
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding
Jungbin Cho, Junwan Kim, Jisoo Kim +5
Human motion is inherently continuous and dynamic, posing significant challenges for generative models. While discrete generation methods are widely used, they suffer from limited…