Liu, JingyuanZhou, XurenFu, HongboTai, Chiew-LanFu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes2018-10-072018-10-072018978-3-03868-074-1https://doi.org/10.2312/pg.20181289https://diglib.eg.org:443/handle/10.2312/pg20181289We present TAVE, a framework that allows novice users to add interesting visual effects by mimicking human actions in a given template video, in which pre-defined visual effects have already been associated with specific human actions. Our framework is mainly based on high-level features of human pose extracted from video frames, and uses low-level image features as the auxiliary information. We encode an action into a set of code sequences representing joint motion directions and use a finite state machine to recognize the action state of interest. The visual effects, possibly with occlusion masks, can be automatically transferred from the template video to a target video containing similar human actions.10.2312/pg.201812893-4