Abstract
Generative artificial intelligence has witnessed remarkable advancements across multiple domains in recent years. Building on the successes of 2D and 3D content generation, 4D generation, which incorporates the temporal dimension into generative tasks, has emerged as a burgeoning yet rapidly evolving research area. This paper presents a comprehensive survey of this emerging field, systematically examining its theoretical foundations, key methodologies, and practical applications, with the aim of providing readers with a holistic understanding of the current state and future potential of 4D generation. We begin by introducing the core concepts of 4D data representations, encompassing both structured and unstructured formats, and their implications for generative tasks. Building upon this foundation, we delve into the enabling technologies that drive 4D generation, including advancements in spatiotemporal modeling, neural representations, and generative frameworks. We further review recent studies that employ diverse control mechanisms and representation strategies for generating 4D outputs, categorizing these approaches and summarizing their research trajectories. In addition, we explore the wide-ranging applications of 4D generation techniques, spanning dynamic object modeling, scene generation, digital human synthesis, 4D content editing, and autonomous driving. Finally, we analyze the key challenges inherent to 4D generation, such as data availability, computational efficiency, and spatiotemporal consistency, and propose promising directions for future research. Our code is publicly available at: \href{this https URL}{this https URL}.
Abstract (translated)
近年来，生成式人工智能在多个领域取得了显著进展。继二维和三维内容生成的成功之后，四维生成作为一种新兴但迅速发展的研究领域应运而生，它将时间维度融入到生成任务中。本文旨在对这一新兴领域的现状进行全面的综述，系统地审视其理论基础、关键方法和技术应用，为读者提供关于四维生成当前状态和未来潜力的整体理解。我们首先介绍四维数据表示的核心概念，包括结构化和非结构化格式，并探讨它们在生成任务中的影响。在此基础上，我们将深入研究推动四维生成的技术，如时空建模的进步、神经网络表示以及生成框架的发展。此外，本文还回顾了最近使用多种控制机制和表示策略来生成四维输出的研究工作，对这些方法进行分类并总结其研究路径。我们进一步探讨四维生成技术的广泛应用领域，包括动态对象建模、场景生成、数字人类合成、四维内容编辑及自动驾驶等。最后，本文分析了四维生成所面临的挑战，如数据可用性、计算效率和时空一致性，并提出未来研究的潜在方向。 我们的代码可在以下链接获取：[此 HTTPS URL](this https URL)。
URL
https://arxiv.org/abs/2503.14501
