Work

Reproducibility Companion Paper: Aesthetics-Driven Virtual Time-Lapse Photography Generation

Year: 2024

Type: conference-paper

Abstract: 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.

Source: ACM International Conference on Multimedia (ACM MM)

DOI: 10.1145/3664647.3685298

Authors: Xin Jin, Longteng Jiang, Yihao Zhang, Lihua Lu, Xiaobo Gao, Boyan Dong

Institutions: Beijing Electronic Science and Technology Institute, Baidu (China)

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province

Awards: 可解释的视觉美学计算模型研究, G7273244328