Abstract
Existing optical flow datasets focus primarily on real-world simulation or synthetic human motion, but few are tailored to Celluloid(cel) anime character motion: a domain with unique visual and motion characteristics. To bridge this gap and facilitate research in optical flow estimation and downstream tasks such as anime video generation and line drawing colorization, we introduce LinkTo-Anime, the first high-quality dataset specifically designed for cel anime character motion generated with 3D model rendering. LinkTo-Anime provides rich annotations including forward and backward optical flow, occlusion masks, and Mixamo Skeleton. The dataset comprises 395 video sequences, totally 24,230 training frames, 720 validation frames, and 4,320 test frames. Furthermore, a comprehensive benchmark is constructed with various optical flow estimation methods to analyze the shortcomings and limitations across multiple datasets.
Abstract (translated)
现有的光学流数据集主要关注现实世界的模拟或合成人体运动，但很少有数据集专门针对赛璐珞（cel）动画角色的运动：这是一个具有独特视觉和运动特征的领域。为了填补这一空白，并促进光学流估算以及下游任务如动漫视频生成和线条画着色的研究，我们引入了LinkTo-Anime，这是第一个专门为使用3D模型渲染的赛璐珞动画角色运动设计的高质量数据集。LinkTo-Anime提供了丰富的注释，包括前向和后向光学流、遮挡掩码以及Mixamo骨架信息。该数据集包含395个视频序列，总计24,230帧用于训练，720帧用于验证，以及4,320帧用于测试。此外，还构建了一个全面的基准测试平台，使用各种光学流估算方法来分析不同数据集中的不足和限制。
URL
https://arxiv.org/abs/2506.02733
