3 papers
cs.CV2026
Plan-and-Verify Video Reward Reasoning with Spatio-Temporal Scene Graph Grounding
Hyomin Kim, Junghye Kim, Joanie Hayoun Chung +4
Reward models for text-to-video (T2V) generation guide post-training but often fail at fine-grained semantic alignment. We trace this to two structural weaknesses in existing reaso…
cs.CV2026
FiRe: Fine-grained Multimodal Reasoning for Enhanced Image Generation
Yongjin Kim, Yoonjin Oh, Yerin Kim +5
With the rapid progress of Multimodal Large Language Models (MLLMs), unified MLLMs that jointly perform image understanding and generation have advanced significantly. However, des…
cs.CV2024
MEVG: Multi-event Video Generation with Text-to-Video Models
Gyeongrok Oh, Jaehwan Jeong, Sieun Kim +4
We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not requ…