Exploring the Frontiers of Animation Video Generation in the Sora Era: Method, Dataset and Benchmark
Yudong Jiang, Baohan Xu, Siqian Yang, Mingyu Ying, Jing Liu, Chao Xu, Siqi Wang, Yidi Wu
Abstract
Animation has gained significant interest in the recent film and TV industry. Despite the success of advanced video generation models like Sora, Kling, and CogVideoX in generating natural videos, they lack the same effectiveness in handling animation videos. Evaluating animation video generation is also a great challenge due to its unique artist styles, violating the laws of physics and exaggerated motions. In this paper, we present a comprehensive system, AniSora, designed for animation video generation, which includes a data processing pipeline, a controllable generation model, and an evaluation benchmark. Supported by the data processing pipeline with over 10M high-quality data, the generation model incorporates a spatiotemporal mask module to facilitate key animation production functions such as image-to-video generation, frame interpolation, and localized image-guided animation. We also collect an evaluation benchmark of 948 various animation videos, with specifically developed metrics for animation video generation. Our entire project is publicly available on https://github.com/bilibili/Index-anisora/tree/main
BibTeX
@inproceedings{ijcai2025_exploringthefron,
title = {Exploring the Frontiers of Animation Video Generation in the Sora Era: Method, Dataset and Benchmark},
author = {Yudong Jiang and Baohan Xu and Siqian Yang and Mingyu Ying and Jing Liu and Chao Xu and Siqi Wang and Yidi Wu and Bingwen Zhu and Yue Zhang and Jinlong Hou and Huyang Sun},
booktitle = {IJCAI 2025},
year = {2025}
}