Lune

CVPR2025Top-tier venue

ByTheWay: Boost Your Text-to-Video Generation Model to Higher Quality in a Training-free Way

Jiazi Bu, Pengyang Ling, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang

2025Year
3Top-tier citations

Abstract

CPII under InnoHK, A dog, playing on the grass, soft lightening, high quality, ... A panda plays in the snow covered forest, 4k resolution, film grain, ... Vanilla ByTheWay Vanilla ByTheWay Enhancing structural plausibility and temporal consistency Enriching motion patterns and amplifying the motion magnitude Figure 1. Unlock the potential of pretrained text-to-video (T2V) generation models in a training-free approach. (1) ByTheWay helps to enhance structural plausibility and temporal consistency in generated videos, significantly reducing artifacts and flickering. (2) ByTheWay contributes to enriching motion patterns and amplifying the motion magnitude in generated videos. Further, ByTheWay can be seamlessly integrated into various powerful T2V backbones (e.g., AnimateDiff[12] and VideoCrafter2[6]) in a plug-and-play manner, serving as a highly extensible module without introducing additional parameters or sampling cost.

Ask about this paper

Your agent reads all of it.

Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.

Questions to start from

Your agent calls

Luneget_paper_fulltext

Ask in Lune

Free to start. No credit card required.

lune papers fulltext ff889636-2de3-4c01-ad30-bbb7c3121718

Cited by top-tier papers3

Ask how each one uses it

Builds on24

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines