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.

中文翻译：

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使用生成建模方法自动创建视频博客内容的新颖之处

生成模型已成为创建高质量图像、视频和文本的潜在工具。本文探讨了生成模型在自动化视频博客内容创建中的应用。它同时处理静态和动态视觉元素，无需人工干预。传统的视频博客通常需要特定的环境条件和适当的照明来创建视频博客。为了简化这个过程，这里提出了一个利用生成模型的自动化系统。生成模型擅长生成与现实世界内容无缝集成的真实内容。它们通过生成新的场景和背景来提高整体视频质量并引入创意元素。本文根据框架元素和前景-背景条件对各种生成建模技术进行了分类。它提供了针对特定目标定制的不同生成模型变体的比较分析。此外，本文还回顾了视频和图像内容生成、视觉质量增强、多样性和连贯性结果的生成模型的现有研究。此外，本文还重点介绍了生成模型在各种上下文中进行内容创建的实际用途，例如面部交换、场景翻译和虚拟内容插入。该论文还研究了用于训练生成模型的公共数据集。这些数据集包含不同的视觉内容，例如名人图像、城市景观和日常场景。