7 papers · 1 filter
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…
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-…
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…
EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolation
Cong Wang, Jiaxi Gu, Panwen Hu +5
Following the advancements in text-guided image generation technology exemplified by Stable Diffusion, video generation is gaining increased attention in the academic community. Ho…
DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance
Cong Wang, Jiaxi Gu, Panwen Hu +3
Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided i…
MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing
Haoyu Zhao, Tianyi Lu, Jiaxi Gu +5
The diffusion model is widely leveraged for either video generation or video editing. As each field has its task-specific problems, it is difficult to merely develop a single diffu…