Abstract
Generating 3D models lies at the core of computer graphics and has been the focus of decades of research. With the emergence of advanced neural representations and generative models, the field of 3D content generation is developing rapidly, enabling the creation of increasingly high-quality and diverse 3D models. The rapid growth of this field makes it difficult to stay abreast of all recent developments. In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications. Specifically, we introduce the 3D representations that serve as the backbone for 3D generation. Furthermore, we provide a comprehensive overview of the rapidly growing literature on generation methods, categorized by the type of algorithmic paradigms, including feedforward generation, optimization-based generation, procedural generation, and generative novel view synthesis. Lastly, we discuss available datasets, applications, and open challenges. We hope this survey will help readers explore this exciting topic and foster further advancements in the field of 3D content generation.
Overview
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The paper surveys recent advancements in AI-driven 3D model generation, synthesizing contributions from computer vision, graphics, and machine learning.
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It discusses different 3D representations (explicit, implicit, hybrid) and generation methods including GANs, diffusion models, autoregressive models, and optimization-based approaches.
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A review of datasets for training 3D generation models is provided, with an emphasis on diversity and size for improving model quality and variety.
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Applications of 3D generation span creating photorealistic human avatars to general object and scene creation, highlighting the field's progress in textured model generation.
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The survey outlines open challenges in 3D content generation around evaluation metrics, data scarcity, and other areas, suggesting opportunities for future research.
Introduction
The synthesis of 3D models is an intricate task that involves the convergence of computer vision, graphics, and machine learning disciplines. The impetus behind generating 3D content spans several domains, from entertainment to virtual reality, necessitating a rich repository of 3D assets. While traditional methods of 3D content creation involved labor-intensive modeling by artists, recent strides in AI have paved the way for automated, high-quality, and scalable 3D model generation.
3D Representations and Generation Methods
At the core of 3D generation are the representations that embody the geometry and appearance of 3D objects. The survey highlights three fundamental types of scene representations: explicit, implicit, and hybrid. Explicit representations describe scenes with primitives like point clouds and meshes, which are straightforward but may lack finer resolution. Implicit representations like Neural Radiance Fields (NeRFs) encapsulate volumetric characteristics and enable continuous and detailed modeling, albeit with slower optimization. Hybrid representations attempt to integrate the strengths of both explicit and implicit forms, offering efficient optimization and flexible topology.
The core methodologies in 3D generation also vary, ranging from generative adversarial networks (GANs) and diffusion models to autoregressive models and optimization-based approaches. GANs, for instance, have shown remarkable success in synthesizing realistic textures and geometries, while diffusion models have demonstrated potential in capturing the underlying structure and randomness inherent in natural objects. Autoregressive models have been used effectively for sequential generation of 3D points or polygons, whereas optimization-based approaches are adept at leveraging pre-existing large-scale models to distill 3D content from textual or image-based prompts.
Datasets and Applications
Training models for 3D generation invariably requires a substantial amount of data. This survey provides an insightful rundown of datasets tailored for different facets of 3D vision, outlining those aimed at object-centric applications such as ShapeNet, to those capturing multi-view images like ScanNet and datasets focused on single-view images including FFHQ and AFHQ. The advent of larger, more diverse datasets such as Objaverse-XL indicates a trend towards enhancing 3D model quality and variety through enriched training sources.
3D generation finds utility in various applications, from generating photorealistic human avatars and facial structures to general object and scene creation. The evolution from textureless models to fully textured assets epitomizes the advancements in the field, offering promising perspectives for practical applications and broader creative possibilities.
Open Challenges
Despite considerable progress, there remain challenges that prevent 3D generated content from fully meeting industry standards. The survey identifies evaluation metrics, data scarcity, content representation, controllability, and the advent of large-scale models as areas necessitating further research. The discussion of these open challenges underscores the complexities of 3D content generation while inviting innovative solutions and perspectives.
Conclusion
This comprehensive survey meticulously presents the dynamic landscape of 3D content generation, offering a structured compilation of methodologies, datasets, applications, and challenges. The synergy of diverse algorithmic paradigms and representation strategies highlighted in the survey not only reflects the current state of the field but also kindles the potential for future breakthroughs that could transform the way we create and interact with 3D content.
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