Author(s): Vedant Singh
The evolution of generative models has been quite rapid, and this has greatly impacted the field of 3D scene generation. It was previously impossible to build such detailed 3D scenes automatically and with relative simplicity because the process demanded considerable human input alongside significant computational power. At the same time, it could be a labor-intensive task, especially when a large number of scenes needed to be generated. New generative synthesis methods such as diffusion models and GANs have made it much easier and more efficient to synthesize high-quality 3D scenes. Of these, the diffusion models have turned out to excel at crafting diverse and highly complex multi-object 3D scenes that are photorealistic and provide a new record in scene generation. Such technologies have enabled new opportunities across industries such as VR, augmented reality, gaming, robotics, and automation, where approximate and more realistic 3D models are increasingly becoming useful. The diffusion models, notably, show comparatively high performance in creating complex spatial patterns and a high level of detail, which is beneficial for realistic applications. This paper discusses the basics, uses, limitations, and innovations of current generative models, with an emphasis on diffusion models, with a view to applying them mainly to 3D scene generation. Thus, this review highlights the possibilities and oversights of diffusion models to demonstrate the possibilities of the future of automated 3D content creation across several technological domains.
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