Motion Part-Level Interpolation and Manipulation over Automatic Symbolic Labanotation Annotation

Junkun Jiang, Ho Yin Au, Jie Chen, Jingyu Xiang, Mingyuan Chen

Abstract: Motion sequencing is a crucial process in creating smooth and natural animations by arranging individual motion sequences based on desired action scripts. Existing methods either rely on carefully engineered key-frame libraries or implicitly encoded latent phase manifolds for sequential interpolation and manipulation. However, ensuring smooth and natural transitions becomes challenging when dealing with complex and diverse actions, and the manipulation flexibility is limited to the frame level. In this study, we introduce a novel motion sequencing framework centered around Labanotation. The framework leverages automatically annotated Labanotation for explicit representation of motion elements to the body-part level. The proposed Laban Masked Autoencoder (LBN-MAE) is able to directly complete, interpolate and translate Laban symbols into natural 3D trajectories. Our framework offers a compact and descriptive representation of motion, enabling precise motion control and reediting. Comparative evaluations against both conventional and state-of-the-art learning-based methods validate the effectiveness of our proposed framework.