PoseVEC: Authoring Adaptive Pose-aware Effects Using Visual Programming and Demonstrations

PoseVEC: Authoring Adaptive Pose-aware Effects Using Visual Programming and Demonstrations
Yongqi Zhang, Cuong Nguyen, Rubaiat Habib Kazi, Lap-Fai Yu

UIST 2023: The ACM International Symposium on User Interface Software and Technology
Session: Words and Visuals: Authoring Tools for Text and Images

Pose-aware visual effects where graphics assets and animations are rendered reactively to the human pose have become increasingly popular, appearing on mobile devices, the web, or even head-mounted displays like AR glasses. Yet, creating such effects still remains difficult for novices. In a traditional video editing workflow, a creator could utilize keyframes to create expressive but non-adaptive results which cannot be reused for other videos. Alternatively, programming-based approaches allow users to develop interactive effects, but are cumbersome for users to quickly express their creative intents. In this work, we propose a lightweight visual programming workflow for authoring adaptive and expressive pose effects. By combining a programming by demonstration paradigm with visual programming, we simplify three key tasks in the authoring process: creating pose triggers, designing animation parameters, and rendering. We evaluated our system with a qualitative user study and a replicated example study, finding that all participants can create effects efficiently.

Web:: https://programs.sigchi.org/uist/2023/program/content/126653

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