Video Generation with Consistency Tuning
Chaoyi Wang Yaozhe Song Yafeng Zhang Jun Pei Lijie Xia Jianpo Liu
Currently, various studies have been exploring generation of long videos.
However, the generated frames in these videos often exhibit jitter and noise.
Therefore, in order to generate the videos without these noise, we propose a
novel framework composed of four modules: separate tuning module, average
fusion module, combined tuning module, and inter-frame consistency module. By
applying our newly proposed modules subsequently, the consistency of the
background and foreground in each video frames is optimized. Besides, the
experimental results demonstrate that videos generated by our method exhibit a
high quality in comparison of the state-of-the-art methods.