Single-Shot Motion Completion with Transformer

TL;DR

This paper introduces a transformer-based unified approach for various motion completion tasks, achieving state-of-the-art accuracy and real-time performance, with applications demonstrated in music-dance scenarios.

Contribution

The work presents a novel transformer framework that handles multiple motion completion scenarios within a single model, improving accuracy and efficiency over prior case-specific methods.

Findings

Achieves state-of-the-art accuracy across multiple motion completion benchmarks.

Operates in real-time with non-autoregressive prediction.

Effectively applied to music-dance motion synthesis.

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.