Video editors often record multiple versions of a performance with minor differences. When they add graphics atop one video, they may wish to transfer those assets to another recording, but differences in performance, wordings, and timings can cause assets to no longer be aligned with the video content. Fixing this is a time consuming, manual task. We present a technique which preserves the temporal and spatial alignment of the original composition when automatically retargeting speech-driven video compositions. It can transfer graphics between both similar and dissimilar performances, including those varying in speech and gesture. We use a large language model for transcript-based temporal alignment and integer programming for spatial alignment. Results from retargeting between 51 pairs of performances show that we achieve a temporal alignment success rate of 90% compared to hand-generated ground truth compositions. We demonstrate challenging scenarios, retargeting video compositions across different people, aspect ratios, and languages.
https://dl.acm.org/doi/10.1145/3706598.3713857
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)