activity
20232025
most citedReuse and Diffuse: Iterative Denoising for Text-to-Video Generation

9 citations · 9 across the 3 of their papers we have counts for

collaborators

5 papers

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

AutoTVG: A New Vision-language Pre-training Paradigm for Temporal Video Grounding

Xing Zhang, Jiaxi Gu, Haoyu Zhao +6

Temporal Video Grounding (TVG) aims to localize a moment from an untrimmed video given the language description. Since the annotation of TVG is labor-intensive, TVG under limited s…

cs.CV2023

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…

cs.CV2023

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…

cs.CV20239 cited

Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation

Jiaxi Gu, Shicong Wang, Haoyu Zhao +7

Inspired by the remarkable success of Latent Diffusion Models (LDMs) for image synthesis, we study LDM for text-to-video generation, which is a formidable challenge due to the comp…