In this paper, we propose SDAS, a new motion data assessment and storage system designed to acquire new motion data with reduced redundancy and maximizing diversity. SDAS collects data in the field, retrieves the most similar data from the database in real-time, and provides visualization tools that allow for the comparison of differences between the capture data and the stored data. Through this system, researchers can efficiently build and manage a database. The demonstration video is available at https://youtu.be/vqW0uMDnZTw.

In this demonstration, we present Permission to Dance, an end-to-end dance enhancement system designed to capture, enhance, and analyze user's dance performance. Our system consists of a dance capture module, a dance enhancement module, and a dance feedback module. Using the system, users can acquire their dance data in an enhanced version, followed by textual feedback on how to achieve better dance performance. The demonstration video is available at https://youtu.be/lFw7Xic48KU

Existing 3D face alignment primarily aim to achieve accurate face alignment result for a static facial image. While these methods have strong alignment performance under large poses, occlusion, and extreme lighting conditions, they often result in trembling artifacts in video-based sequential 3D face alignment. Reducing temporal misalignment remains a challenging task because a single misaligned frame can propagate errors to other frames along the temporal axis. To address this issue, we propose a novel temporal discriminating scheme that learns the distribution gap between the face alignment results and ground truth face animation. By leveraging the discrimination results as a guide, the proposed method can effectively align the 3D faces to the input video by reducing temporal trembling artifacts. To effectively learn the distribution gap, we introduce a multi-discriminating scheme that separately discriminates facial animation based on identity and expression changes. It enables the proposed method to produce a stabilized alignment result, especially in dynamic and fast movement. Through extensive experiments in both qualitative and quantitative evaluations, it is confirmed that our method outperforms state-of-the-art 3D face alignment methods by animating stabilized results in the video.