Chang-E: A High-Quality Motion Capture Dataset of Chinese Classical Dunhuang Dance

Abstract

Derived from the mural drawings in the UNESCO-listed Mogao Caves, Dunhuang dance has unique cultural value but faces challenges of digitization and preservation. In this paper, we introduce the first open comprehensive motion capture dataset of Dunhuang dance, Chang-E, including full-body movements documented across eight categories, totaling 40 minutes of professional dance (preview available at https://cislab.hkust-gz.edu.cn/projects/chang-e/). This dataset contains three formats: skeleton data acquired from motion capture, body mesh generated from skeleton using machine learning, and multiview videos recorded on site. The dataset supports various creative applications for Dunhuang dance culture, as demonstrated by an immersive new media exhibition. Through the curation process, we applied motion inbetweening algorithms to concatenate different dance sequences for choreography. Also, these reinterpreted dance sequences are synchronized with music using retiming techniques, augmenting the rhythms and harmony between the music and dance performance. Furthermore, we applied visual effects on the regenerated motion sequences of digital dancers, achieving artistic and appealing visual results echoing Buddhist discourses of meditation and bodily cognition. The Chang-E dataset enables digital preservation and creative reimagination of Dunhuang dance, offering not only high-quality data but also an interdisciplinary collaboration framework for future graphics and cultural heritage research.