This week, I improved the game’s UI/UX pipeline to facilitate smooth selection of reference .mp4 videos. Although initial implementation was partially completed last week, several bugs affecting the UI/UX integration were identified and resolved this week. Users can now intuitively pick and load a video directly from the game interface, simplifying the setup process and enhancing the overall user experience. Furthermore, the video analysis module was extended to handle selected reference videos robustly, effectively translating video movements into coordinates used by the avatar. This enhancement enables accurate real-time performance comparison, seamlessly integrating both live capture and pre-recorded video data.
This is Danny in a Pre-recorded .mp4 (reference mp4) – NOT live capture
Additionally, I successfully optimized the avatar‘s leg recreation for our Unity-based OpenCV MediaPipe dance comparison game. Previously, the avatar’s leg movements experienced slight jitter and occasional lagging frames, making the visual representation less smooth as I mentioned in last week’s report. By refining the landmark smoothing algorithm and employing interpolation techniques between key frames, the avatar’s leg animations now follow the user’s movements better, significantly enhancing overall realism and responsiveness. As a result, the visual feedback loop maintains an ideal frame rate, consistently hovering around 30 fps, matching our outlined design goals.
Currently, our progress aligns well with our original timeline. Next week, I plan to focus on optimizing and integrating the comparison algorithm further alongside Danny and Akul. Our goal is to implement more sophisticated analytical metrics to assess player accuracy comprehensively. Deliverables targeted for completion include a refined comparison algorithm fully integrated into our Unity game pipeline, rigorous testing, and initial documentation outlining the improved analytic metrics.