In this paper, we replicate the experimental results from our previous work titled "Aesthetics-Driven Virtual Time-Lapse Photography Generation", which was presented at ACM Multimedia 2023. Our primary objective is to confirm the validity of our earlier findings and to provide a more comprehensive understanding of our software framework. We provide the necessary artifacts to reproduce the results from our prior research. This paper details the technical aspects of our package, including dataset preparation, source code structure, and the experimental environment. By utilizing these artifacts, we demonstrate the reproducibility of our results. We encourage others to use our software framework for purposes beyond reproducibility.

Time-lapse videos can visualize the temporal change of dynamic scenes and present wonderful sights with drastic variance in color appearance and rapid movement that interests people. We propose an aesthetics-driven virtual time-lapse photography framework to explore the automatic generation of time-lapse videos in the virtual world, which has potential applications like artistic creation and entertainment in the virtual space. We first define shooting parameters to parameterize the time-lapse photography process and accordingly propose image, video, and time-lapse aesthetic assessments to optimize these parameters, enabling the process to be autonomous and adaptive. We also build an interactive interface to visualize the shooting process and help users conduct virtual time-lapse photography by personalizing shooting parameters according to their aesthetic preferences. Finally, we present a two-stream time-lapse aesthetic model and a time-lapse aesthetic dataset, which can evaluate the aesthetic quality of time-lapse videos. Experimental results demonstrate our method can automatically generate time-lapse videos comparable to those of professional photographers and is more efficient.