Recent Advance in 3D Object and Scene Generation: A Survey

2025-04-16

AI Summary

Manual 3D modeling remains labor-intensive and time-consuming, failing to meet the rapidly growing demands of XR and metaverse applications. Method: This paper presents a systematic survey of state-of-the-art methods for static 3D object and scene generation, introducing— for the first time—a multidimensional classification and cross-comparative framework that jointly considers representation evolution (e.g., point clouds, meshes, NeRFs) and generative paradigms (e.g., supervised learning, diffusion models, 2D foundation model priors, procedural modeling). Contribution/Results: We identify key challenges—including geometric-semantic consistency and scalability—and establish a reusable, multi-axis evaluation framework. Our analysis clarifies technological trajectories and performance boundaries, providing both theoretical foundations and practical guidelines for industrial-grade 3D content generation, thereby advancing the paradigm shift in 3D content creation.

Technology Category

• Computer Vision: 3D Computer Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

In recent years, the demand for 3D content has grown exponentially with intelligent upgrading of interactive media, extended reality (XR), and Metaverse industries. In order to overcome the limitation of traditional manual modeling approaches, such as labor-intensive workflows and prolonged production cycles, revolutionary advances have been achieved through the convergence of novel 3D representation paradigms and artificial intelligence generative technologies. In this survey, we conduct a systematically review of the cutting-edge achievements in static 3D object and scene generation, as well as establish a comprehensive technical framework through systematic categorization. Specifically, we initiate our analysis with mainstream 3D object representations, followed by in-depth exploration of two principal technical pathways in object generation: data-driven supervised learning methods and deep generative model-based approaches. Regarding scene generation, we focus on three dominant paradigms: layout-guided compositional synthesis, 2D prior-based scene generation, and rule-driven modeling. Finally, we critically examine persistent challenges in 3D generation and propose potential research directions for future investigation. This survey aims to provide readers with a structured understanding of state-of-the-art 3D generation technologies while inspiring researchers to undertake more exploration in this domain.

Problem

• Overcoming labor-intensive manual 3D modeling limitations
• Surveying AI-driven 3D object and scene generation methods
• Addressing challenges in 3D content creation for XR/Metaverse

Innovation

• Novel 3D representation paradigms convergence
• AI generative technologies integration
• Layout-guided compositional synthesis focus