Papers › ModelScope Text-to-Video Technical Report

ModelScope Text-to-Video Technical Report

12 Aug 2023arXiv:2308.06571archive 2025-07-28

Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang

This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-temporal blocks to ensure consistent frame generation and smooth movement transitions. The model could adapt to varying frame numbers during training and inference, rendering it suitable for both image-text and video-text datasets. ModelScopeT2V brings together three components (i.e., VQGAN, a text encoder, and a denoising UNet), totally comprising 1.7 billion parameters, in which 0.5 billion parameters are dedicated to temporal capabilities. The model demonstrates superior performance over state-of-the-art methods across three evaluation metrics. The code and an online demo are available at \url{https://modelscope.cn/models/damo/text-to-video-synthesis/summary}.

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exponentialml/text-to-video-finetuning officialmentioned in papermentioned on GitHubpytorchMIT report
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Tasks

DenoisingImage GenerationText-to-Video Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Text-to-Video Generation MSR-VTT ModelScopeT2V CLIPSIM 0.2930 #9 of 18 Archive leaderboard report
Text-to-Video Generation MSR-VTT ModelScopeT2V FID 11.09 #9 of 18 Archive leaderboard report
Text-to-Video Generation MSR-VTT ModelScopeT2V FVD 550 #9 of 18 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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