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Programmatic Concept Learning for Human Motion Description and Synthesis

2022-06-27 17:59:57
Sumith Kulal, Jiayuan Mao, Alex Aiken, Jiajun Wu

Abstract

We introduce Programmatic Motion Concepts, a hierarchical motion representation for human actions that captures both low-level motion and high-level description as motion concepts. This representation enables human motion description, interactive editing, and controlled synthesis of novel video sequences within a single framework. We present an architecture that learns this concept representation from paired video and action sequences in a semi-supervised manner. The compactness of our representation also allows us to present a low-resource training recipe for data-efficient learning. By outperforming established baselines, especially in the small data regime, we demonstrate the efficiency and effectiveness of our framework for multiple applications.

Abstract (translated)

URL

https://arxiv.org/abs/2206.13502

PDF

https://arxiv.org/pdf/2206.13502.pdf


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