Data-driven modelling and synthesis of motion data is an active research area with applications that include animation and games. This paper introduces a new class of probabilistic, generative motion-data models based on normalising flows, specifically Glow. Models of this kind can describe highly complex distributions (unlike many classical approaches like GMMs) yet can be trained stably and efficiently using standard maximum likelihood (unlike GANs). Several model variants are described: unconditional fixed-length sequence models, conditional (i.e., controllable) fixed-length sequence models, and finally conditional, variable-length sequence models. The last type uses LSTMs to enable arbitrarily long time-dependencies and is, importantly, causal, meaning it only depends on control and pose information from current and previous timesteps. This makes it suitable for generating controllable motion in real-time applications. Every model type can in principle be applied to any motion since they do not make restrictive assumptions such as the motion being cyclic in nature. Experiments on a motion-capture dataset of human locomotion confirm that motion (sequences of 3D joint coordinates) sampled randomly from the new methods is judged as convincingly natural by human observers.

本文提出了一种新的基于正则化流的概率、生成和可控运动数据模型，该模型具有高度复杂的分布描述能力，可以使用ML精确地进行训练，同时避免了GANs或VAEs的算法延迟，并在单个输出序列中自动生成每个姿态，评估结果证明其运动质量接近记录的运动捕捉。

MoGlow：使用正规化流进行概率和可控的运动合成