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arXiv · 2103.00776

Single-Shot Motion Completion with Transformer

Yinglin Duan·Tianyang Shi·Zhengxia Zou·Yenan Lin·Zhehui Qian·Bohan Zhang·Yi Yuan
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Abstract

Motion completion is a challenging and long-discussed problem, which is of great significance in film and game applications. For different motion completion scenarios (in-betweening, in-filling, and blending), most previous methods deal with the completion problems with case-by-case designs. In this work, we propose a simple but effective method to solve multiple motion completion problems under a unified framework and achieves a new state of the art accuracy under multiple evaluation settings. Inspired by the recent great success of attention-based models, we consider the completion as a sequence to sequence prediction problem. Our method consists of two modules - a standard transformer encoder with self-attention that learns long-range dependencies of input motions, and a trainable mixture embedding module that models temporal information and discriminates key-frames. Our method can run in a non-autoregressive manner and predict multiple missing frames within a single forward propagation in real time. We finally show the effectiveness of our method in music-dance applications.

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Yinglin Duan, Tianyang Shi, Zhengxia Zou, Yenan Lin, Zhehui Qian, Bohan Zhang, Yi Yuan. 2021-03-01. Single-Shot Motion Completion with Transformer. https://arxiv.org/abs/2103.00776

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