Papers › Towards Smooth Video Composition

Towards Smooth Video Composition

14 Dec 2022arXiv:2212.07413archive 2025-07-28

Qihang Zhang, Ceyuan Yang, Yujun Shen, Yinghao Xu, Bolei Zhou

Video generation requires synthesizing consistent and persistent frames with dynamic content over time. This work investigates modeling the temporal relations for composing video with arbitrary length, from a few frames to even infinite, using generative adversarial networks (GANs). First, towards composing adjacent frames, we show that the alias-free operation for single image generation, together with adequately pre-learned knowledge, brings a smooth frame transition without compromising the per-frame quality. Second, by incorporating the temporal shift module (TSM), originally designed for video understanding, into the discriminator, we manage to advance the generator in synthesizing more consistent dynamics. Third, we develop a novel B-Spline based motion representation to ensure temporal smoothness to achieve infinite-length video generation. It can go beyond the frame number used in training. A low-rank temporal modulation is also proposed to alleviate repeating contents for long video generation. We evaluate our approach on various datasets and show substantial improvements over video generation baselines. Code and models will be publicly available at https://genforce.github.io/StyleSV.

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Image GenerationVideo GenerationVideo Understandingsingle-image-generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Video Generation Sky Time-lapse StyleSV (256x256) FVD 16 49.0 #1 of 9 Archive leaderboard report
Video Generation Taichi StyleSV (256x256) FVD16 82.6 #1 of 6 Archive leaderboard report
Video Generation YouTube Driving StyleSV FVD16 207.2 #1 of 1 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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