Synchronized Video Output via User Pose Tracking
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Solution Overview
Problem
Users face difficulties in comprehending fitness exercise instructions due to synchronization issues between their movements and the video output, as the position, rate of movement, and viewpoint of the instructor differ from their own, leading to a negative user experience.
Innovation Solution
A system that generates synchronized video output by analyzing video data from a camera to determine the user's position, rate of movement, and viewpoint, matching it with corresponding frames of an instructor's video to ensure alignment and optimal viewing perspective, using techniques like 3D pose estimation and optical flow analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the video output shows the instructor from a fixed viewpoint and at a fixed rate of movement, then the video content is simple to produce and play back, but the user comprehension is hindered when the user's position and movement rate differ from the instructor's
Solution Approach 1:
The video output dynamically adjusts the instructor's pose and viewpoint to match the user's current pose and position. The system continuously tracks the user's movements and synchronizes the video playback accordingly, making the video content adaptive rather than static. This resolves the contradiction by allowing fixed video production while achieving dynamic synchronization through real-time pose estimation and video frame selection.
Solution Approach 2:
The system uses feedback from the user's tracked position and pose to control which video frames are displayed. The user's current state serves as feedback that determines the appropriate instructor pose and viewpoint to show, creating a closed-loop system that improves comprehension while maintaining simple video production.
2Reliability
If the video playback rate is fixed to match the instructor's speed, then the video is easy to produce, but it creates desynchronization when the user moves at a different rate
Solution Approach 1:
The video playback rate is dynamically adjusted based on the user's movement rate. Instead of using a fixed playback rate, the system continuously adapts the video speed to match the user's pace, ensuring synchronization reliability without requiring complex manual intervention or pre-programming for different speeds.
Solution Approach 2:
The system automatically adjusts the video playback rate based on detected user movement, eliminating the need for external control mechanisms. The video player self-regulates its speed based on the user's pace, achieving reliable synchronization through autonomous adaptation rather than complex external control.
3Adaptability or versatility
If multiple video angles are recorded for every instructor pose, then the desired viewpoint can always be matched, but the data storage and processing requirements increase significantly
Solution Approach 1:
Instead of providing all possible viewpoints for all poses, the system records multiple angles only for specific key poses that are most beneficial for user understanding. For other poses, a single viewpoint or no additional angles are recorded. This local quality approach provides viewpoint adaptability where needed while minimizing overall data volume.
Solution Approach 2:
The system records slightly more video angles than strictly necessary for perfect viewpoint matching, focusing on capturing the most important perspectives for key instructional moments. This partial action approach achieves sufficient adaptability without the excessive data storage requirements of complete multi-angle coverage for every pose.
Data Source
AI summary
Video output is generated based on first video data that depicts the user performing an activity. Poses of the user during performance of the activity are compared with second video data that depicts an instructor performing the activity. Corresponding poses of the user's body and the instructor's body may be determined through comparison of the first and second video data. The video data is used to determine the rate of motion of the user and to generate video output in which a visual representation of the instructor moves at a rate similar to the that of the user. For example, video output generated based on an instructional fitness video may be synchronized so that movement of the presented instructor matches the rate of movement of the user performing an exercise, improving user comprehension and performance.