MagicAnime: A Hierarchically Annotated, Multimodal and Multitasking Dataset with Benchmarks for Cartoon Animation Generation
This addresses the problem of scarce public multimodal cartoon data for researchers and developers in animation generation, though it is incremental as it builds on existing dataset creation efforts.
The authors tackled the challenge of generating high-quality cartoon animations by creating the MagicAnime dataset, a large-scale, hierarchically annotated, and multimodal resource with benchmarks, which includes 400k video clips for image-to-video generation and supports multiple tasks, validated through comprehensive experiments.
Generating high-quality cartoon animations multimodal control is challenging due to the complexity of non-human characters, stylistically diverse motions and fine-grained emotions. There is a huge domain gap between real-world videos and cartoon animation, as cartoon animation is usually abstract and has exaggerated motion. Meanwhile, public multimodal cartoon data are extremely scarce due to the difficulty of large-scale automatic annotation processes compared with real-life scenarios. To bridge this gap, We propose the MagicAnime dataset, a large-scale, hierarchically annotated, and multimodal dataset designed to support multiple video generation tasks, along with the benchmarks it includes. Containing 400k video clips for image-to-video generation, 50k pairs of video clips and keypoints for whole-body annotation, 12k pairs of video clips for video-to-video face animation, and 2.9k pairs of video and audio clips for audio-driven face animation. Meanwhile, we also build a set of multi-modal cartoon animation benchmarks, called MagicAnime-Bench, to support the comparisons of different methods in the tasks above. Comprehensive experiments on four tasks, including video-driven face animation, audio-driven face animation, image-to-video animation, and pose-driven character animation, validate its effectiveness in supporting high-fidelity, fine-grained, and controllable generation.