VideoPoseVR

This paper introduces a video-based animation authoring workflow using online videos to author character animations in virtual reality (VR). It leverages the state-of-the-art deep learning approach to reconstruct 3D motions from online videos, caption the motions, and store them in a motion dataset. Creators can import the videos, search in the dataset, modify the motion timeline, and combine multiple motions from videos to author character animations in VR.

Abstract

VideoPoseVR: Authoring Virtual Reality Character Animations with Online Videos

Cheng Yao Wang, Qian Zhou, George Fitzmaurice, Fraser Anderson

ACM International Conference on Interactive Tabletops and Surfaces 2022

We present VideoPoseVR, a video-based animation authoring workflow using online videos to author character animations in VR. It leverages the state-of-the-art deep learning approach to reconstruct 3D motions from online videos, caption the motions, and store them in a motion dataset. Creators can import the videos, search in the dataset, modify the motion timeline, and combine multiple motions from videos to author character animations in VR. We implemented a proof-of-concept prototype and conducted a user study to evaluate the feasibility of the video-based authoring approach as well as gather initial feedback of the prototype. The study results suggest that VideoPoseVR was easy to learn for novice users to author animations and enable rapid exploration of prototyping for applications such as entertainment, skills training, and crowd simulations.