Video aesthetic assessment, a vital area in multimedia computing, integrates computer vision with human cognition. Its progress is limited by the lack of standardized datasets and robust models, as the temporal dynamics of video and multimodal fusion challenges hinder direct application of image-based methods. This study introduces VADB, the largest video aesthetic database with 10,490 diverse videos annotated by 37 professionals across multiple aesthetic dimensions, including overall and attribute-specific aesthetic scores, rich language comments and objective tags. We propose VADB-Net, a dual-modal pre-training framework with a two-stage training strategy, which outperforms existing video quality assessment models in scoring tasks and supports downstream video aesthetic assessment tasks. The dataset and source code are available at https://github.com/BestiVictory/VADB.

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.

This paper introduces a novel method for enhancing image composition guidance in photography. It utilizes advanced composition rules to guide a Real-Time Detection Transformer (RT-DETR) model in predicting aesthetically pleasing compositions for photographs. Unlike traditional methods constrained by original image boundaries, our approach allows the predicted framing to extend beyond these limits, offering dynamic, real-time guidance for image composition in photography. The system integrates multi-label composition classification and compositional element annotation, using YOLOv8 for key object detection and an enhanced Deep Hough Transform for compositional lines to guide photographers. It provides photographers with real-time guidance for optimal camera adjustments, transforming traditional post-processing tasks into an intuitive, interactive process. This method significantly enhances photographers' flexibility and effectiveness in capturing visually superior photographs.

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.