Single-Shot Motion Completion with Transformer

• URL: http://arxiv.org/abs/2103.00776v1
• Date: Mon, 1 Mar 2021 06:00:17 GMT
• Title: Single-Shot Motion Completion with Transformer
• Authors: Yinglin Duan (1), Tianyang Shi (1), Zhengxia Zou (2), Yenan Lin (3), Zhehui Qian (3), Bohan Zhang (3), Yi Yuan (1) ((1) NetEase Fuxi AI Lab, (2) University of Michigan, (3) NetEase)
• Abstract summary: We propose a simple but effective method to solve multiple motion completion problems under a unified framework. Inspired by the recent great success of attention-based models, we consider the completion as a sequence to sequence prediction problem. Our method can run in a non-autoregressive manner and predict multiple missing frames within a single forward propagation in real time.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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