The synthesis of human motion has traditionally been addressed through
task-dependent models that focus on specific challenges, such as predicting
future motions or filling in intermediate poses conditioned on known key-poses.
In this paper, we present a novel task-independent model called UNIMASK-M,
which can effectively address these challenges using a unified architecture.
Our model obtains comparable or better performance than the state-of-the-art in
each field. Inspired by Vision Transformers (ViTs), our UNIMASK-M model
decomposes a human pose into body parts to leverage the spatio-temporal
relationships existing in human motion. Moreover, we reformulate various
pose-conditioned motion synthesis tasks as a reconstruction problem with
different masking patterns given as input. By explicitly informing our model
about the masked joints, our UNIMASK-M becomes more robust to occlusions.
Experimental results show that our model successfully forecasts human motion on
the Human3.6M dataset. Moreover, it achieves state-of-the-art results in motion
inbetweening on the LaFAN1 dataset, particularly in long transition periods.
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UNIMASK-M 模型是一种任务独立模型，有效地解决了预测未来动作、填充已知关键动作之间的中间姿势等挑战，通过将人体姿势分解为身体部位并利用存在于人体运动中的时空关系，将各种面向姿势的运动综合任务重新定义为使用不同屏蔽模式的重构问题。实验结果表明，该模型在 Human3.6M 数据集上成功预测了人体运动，并在 LaFAN1 数据集上实现了时长较长的过渡期内的运动插值的最新成果。