3 papers
cs.CV2026
DCDM: Divide-and-Conquer Diffusion Models for Consistency-Preserving Video Generation
Haoyu Zhao, Yuang Zhang, Junqi Cheng +5
Recent video generative models have demonstrated impressive visual fidelity, yet they often struggle with semantic, geometric, and identity consistency. In this paper, we propose a…
cs.CV2025
Repeating Words for Video-Language Retrieval with Coarse-to-Fine Objectives
Haoyu Zhao, Jiaxi Gu, Shicong Wang +4
The explosive growth of video streaming presents challenges in achieving high accuracy and low training costs for video-language retrieval. However, existing methods rely on large-…
cs.CV2024
Fuse Your Latents: Video Editing with Multi-source Latent Diffusion Models
Tianyi Lu, Xing Zhang, Jiaxi Gu +5
Latent Diffusion Models (LDMs) are renowned for their powerful capabilities in image and video synthesis. Yet, compared to text-to-image (T2I) editing, text-to-video (T2V) editing…