A novel aspect of automatic vlog content creation using generative modeling approaches

• Published in: Digital signal processing Vol. 148; p. 104462
• Main Authors: Kumar, Lalit, Singh, Dushyant Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.05.2024
• Subjects: Computer vision; Deep learning; Generative modeling; Image synthesis; Video synthesis; Deep learning; Computer vision; Generative modeling; Video synthesis; Image synthesis
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2024.104462

Generative models have emerged as potential tools for creating high-quality images, videos, and text. This paper explores the application of generative models in automating vlog content creation. It addresses both static and dynamic visual elements, eliminating the need for human intervention. Traditional vlogs often require specific environmental conditions and proper lighting for the vlog creation. To streamline this process, an automated system utilizing the generative models is proposed here. Generative models excel at generating realistic content that seamlessly integrates with real-world content. They enhance overall video quality and introduce creative elements by generating new scenes and backgrounds. This paper categorizes various generative modeling techniques based on frame elements and foreground-background conditions. It offers a comparative analysis of different generative model variants tailored for specific objectives. Furthermore, the paper reviews existing research on generative models for video and image content generation, visual quality enhancement, diversity, and coherence outcomes. Additionally, the paper highlights practical uses of the generative model for content creation in various contexts, such as face swapping, scene translation, and virtual content insertion. The paper also examines the public datasets used to train generative models. These datasets contain diverse visual content such as celebrity images, urban landscapes, and everyday scenes.