A Unified Framework for Real Time Motion Completion

Motion completion, as a challenging and fundamental problem, is of great significance in film and game applications. For different motion completion application scenarios (inbetweening, in-filling, and blending), most previous methods deal with the completion problems with case-by-case methodology designs. In this work, we propose a simple but effective method to solve multiple motion completion problems under a unified framework and achieve a new state-ofthe-art accuracy on LaFAN1 (+17% better than the previous SoTA) under multiple evaluation settings. Inspired by the recent great success of self-attention-based transformer models, we consider the completion as a sequence-to-sequence prediction problem. Our method consists of three modules a standard transformer encoder with self-attention that learns long-range dependencies of input motions, a trainable mixture embedding module that models temporal information and encodes different key-frame combinations in a unified form, and a new motion perceptual loss for better capturing high-frequency movements. Our method can predict multiple missing frames within a single forward propagation in real-time without post-processing. We also introduce a novel large-scale dance movement dataset for exploring the scaling capability of our method and its effectiveness in complex motion applications.

Authors

• Yinglin Duan
• Yue Lin NetEase Games AI Lab, Guangzhou, China
• Zhengxia Zou
• Yi Yuan Douyin Vision
• Zhehui Qian
• Bohan Zhang

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