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Yinglin Duan

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2 papers

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AAAI Conference 2022 Conference Paper

A Unified Framework for Real Time Motion Completion

Yinglin Duan
Yue Lin
Zhengxia Zou
Yi Yuan
Zhehui Qian
Bohan Zhang

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.

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IJCAI Conference 2021 Conference Paper

Automatic Translation of Music-to-Dance for In-Game Characters

Yinglin Duan
Tianyang Shi
Zhipeng Hu
Zhengxia Zou
Changjie Fan
Yi Yuan
Xi Li

Music-to-dance translation is an emerging and powerful feature in recent role-playing games. Previous works of this topic consider music-to-dance as a supervised motion generation problem based on time-series data. However, these methods require a large amount of training data pairs and may suffer from the degradation of movements. This paper provides a new solution to this task where we re-formulate the translation as a piece-wise dance phrase retrieval problem based on the choreography theory. With such a design, players are allowed to optionally edit the dance movements on top of our generation while other regression-based methods ignore such user interactivity. Considering that the dance motion capture is expensive that requires the assistance of professional dancers, we train our method under a semi-supervised learning fashion with a large unlabeled music dataset (20x than our labeled one) and also introduce self-supervised pre-training to improve the training stability and generalization performance. Experimental results suggest that our method not only generalizes well over various styles of music but also succeeds in choreography for game players. Our project including the large-scale dataset and supplemental materials is available at https: //github. com/FuxiCV/music-to-dance.

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