Industries such as film have seen an increase in the use of 3D models and game engines as details are now crossing the uncanny valley and becoming indistinguishable from real life, leading to advances in experimental filmmaking, including VR cinematics [16] . The mainstream film industry has also seen a rise in the use of traditional game development software like Unreal Engine 5, which has become a tool used by visual effects artists and filmmakers to create realistic worlds in real-time. The American space western television series The Mandalorian [17] was a front runner in standardizing the use of game engine technology as it provided filmmakers the ability to create realistic virtual sets that would dynamically change based on needs and camera position, and also reflect accurate lighting information [18] . The development pipeline saved countless hours in post-processing as much of the effects were done in camera and on set [19] . The process also has been praised by the actors as they can see the world they are acting in as opposed to the previous method of working in front of a green screen [20] . The need for 3D models and virtual sets will only rise as more and more film projects are relying on this technology. As more films are also shot in virtual reality (VR), the impact of these technologies has the potential to radically change how directing in the film industry operates [21] [22] .

With all of the advances made possible using game-engine technology, 3D modeling remains limited to specialists. The development pipeline for 3D modeling explains why [23] . In traditional 3D modeling, the modeler starts with simple geometry, such as a polygon, which can be as simple as a triangle comprised of three vertices existing in three-dimensional space represented in the Cartesian coordinate standard of X, Y, Z. A minimum of three vertices are required to generate the surface geometry, and today’s game models can easily be comprised of 20,000 polygons, which would be the equivalent to 40,000 triangles and those are models that have been optimized for performance. Because of these considerations, the modeling pipeline is heavily reliant on several specialized skills and techniques, such as re-topologizing meshes to improve animation and performance, rendering tools such as normal maps to produce fine details, as well as post-processing effects using specialized shaders [24] . The requirements can lead to the need to be able to process and render thousands, if not millions of polygons on screen at 60 frames per second. The following will investigate the two variables of the asset creation pipeline: the creation of models and the optimization and usability of those models in a game engine or other real-time applications.

The acquisition or creation of 3D models has seen a few developments in the past several years, the most well-known of which is 3D scanning. This technology is not new but continues to improve in both quality and adaptation, as well as accessibility. Whereas previous iterations were large and cumbersome, the latest generation of scanners are handheld (e.g. Artec 3D) or even free applications for smartphones that use LiDAR (e.g. Scaniverse and Polycam) [25] . These scanners and their associated software applications allow users to create 3D models of objects large and small and can even scan entire areas [26] . The technology is used in many industries beyond entertainment including engineering and even law enforcement (Chenoweth et al. 2022). Alternatively, another related method of model creation is photogrammetry, which is the process of generating 3D models from photographs or other data. This process is also not new and began with the creation of 2D information, not only from photographs but also from sonar and radar and has been used to create topographic maps [27] . These processes have expanded to other fields including entertainment and these techniques have been used in films such as The Matrix [28] and video games [29] . Taken together, both 3D scanning and photogrammetry can produce 3D models and assets.

Technology continues to develop, and AI has been used with photogrammetry to improve models and fill in details that were not present in photographs (Amaro et al. 2022). However, recently AI has begun to cross over from being a tool for helping with art creation to a method for generating art. AI art generators like the DALL-E 2 have recently made headlines by creating interesting and imaginative works of art [30] . While these early models often would make mistakes that seem obvious to human eyes, with each iteration the AI improves, and newer models have started to make photo-realistic art [30] . These examples are for creating 2D art but can clearly show an evolution of quality and sophistication with each newer algorithmic iteration. An exciting aspect about using AI is its ability to improve and learn from previous versions. Artists are using these generated images to inspire them and as starting points for conceptualizing ideas and designs, and many speculate it will not be long before AI can do the largest part of concept design work [3] .

The move from 2D art generation to 3D content generation is a natural progression. This has led to several AI systems that can take a prompt, some of which can be as simple as a text description and transform that into a 3D model. Such SOTA models may be used to train AI to understand 3D space using image language models [31] . The open-world 3D scene understanding task is a 3D vision-language task that also includes open-set classification. The limitations of the tasks are that the AI does not currently have enough data. Unfortunately, existing 3D datasets are not varied enough in comparison to 2D counterparts to train AI to generate content [32] . Admittedly, the creation of a 3D model from a text prompt is still early in development and is not ideal for asset creation, but other solutions do currently exist.

Just as photometry uses data to generate pictures, some AI systems are using 2D pictures as inputs to generate 3D content. By loading images as reference, AI systems like Nvidia’s Instant NeRF and Kaedim can generate 3D models. Kaedim is a newer image to 3D model AI tool aimed at 3D artists and those that need 3D models created from concept design. The tool is still in development and currently needs human reviewers to ensure quality of output. The software reviews images of a concept design from all angles and creates a 3D model. Kaedim is one of the few AI 3D model generating tools that takes the technical requirements of the models in mind but does require the user to specify the complexity of the model [33] . This process does require the user to be aware of the specific requirements of the platform or real-time application the model is developed for and experience to know how many polygons would be appropriate [34] . Nvidia’s NeRF (Neural Radiance Field) uses a process based on a concept called inverse rendering, which essentially inverts the concept of normal rendering and attempts to recreate how light reacts to objects in the real world. Instead of normal baked lighting, NeRF instead uses AI to analyze a collection of 2D images and constructs a 3D model from them. The process can create a full 3D scene in a short amount of time [35] . These methods of 3D model creation are becoming increasingly easy to use, and with accessibility becoming as simple as an application accessed on a smartphone, the hurdle of creating a 3D model has all but been removed.

The other variable to consider in the proposed immersive development pipeline is the optimization of 3D models for use in real-time applications. While the methods outlined above can create models often with a high number of details, the models themselves are not in a usable format consisting of dense meshes that would lead to poor performance and be unsuitable for animation [36] . This is an issue even current processes face, as 3D modeling for entertainment often uses a technique called digital sculpture [37] . Many of the most popular modeling applications use these techniques to spectacular effect; the most popular software application for digital sculpting is ZBrush. The process where an artist uses digital sculpture to create a model can also result in a dense unusable mesh that must be re-topologized. Retopology can be a time-consuming process where a lower resolution and optimized version of a model is made, the version that would work well in a game engine, for instance, and then the high-resolution details are added back in during rendering [38] . The process usually includes baking the higher-resolution details into color images where the surface of the high-resolution mesh is represented in a texture where the three channels of red, green, and blue are controlling the XYZ information of how light reacts to the surface of the model. These textures are called normal maps and have been standard practice since the early 2000s [39] .

The process involved with retopology can be rather involved and lengthy, therefore, software developers have been working on ways to make it easier, such as including auto-retopology tools to popular 3D software applications [40] . One of the foremost pioneers of graphics technology in this area is the graphics card manufacturer Nvidia. The AI computing company has been sponsoring and developing new graphics technologies for decades and has an annual technology conference where new graphics technologies are showcased. As so much of the processing and rendering of immersive realities and real-time applications rely on the hardware, Nvidia is also involved in improving the performance of those functions [41] . Two of Nvidia’s recent innovations that are relevant for this study are the Nvidia NGX and aforementioned Nvidia NeRF. Both technologies approach rendering in new and dynamic ways. The Nvidia NGX requires an Nvidia RTX video card and uses that hardware combined with AI and deep learning to improve performance and improve the graphic output. The NGX can load an entire 3D scene from an previous design iteration and create a modern lighting and rendering solution. The process can transform older, lower resolution graphics and ensure that they appear crisp and clear on modern systems and resolutions [42] . While this process can make older graphics appear more modern it is, however, focused on the textural graphics and not the resolution of the models themselves making the solution ideal for lower resolution models. Higher resolution models would still require optimized geometry like that which is done through proper retopology [43] .

In order to address the retopology issue, Epic Games’s engineers working on Unreal Engine 5 have used a new approach of virtualized geometry systems that aims to render retopology unnecessary all together. The Nanite system, which is built on earlier iterations and research on how to process and render high-resolution geometry in real-time, allows the user to bring in non-optimized high-resolution 3D models directly from a 3D scan or highly detailed digital sculpt. These files are able to be altered in size to ensure fast processing and rendering [44] [45] . Nanite does this in several novel ways, including dynamically making level of detail (LOD) in real time, which consist of multiple copies at varied polygonal complexity that decrease in resolution as the viewer moves away from an object and increases in complexity as a viewer moves closer. The virtualized geometry system also automatically occludes polygons that are not visible from the viewer’s perspective, making them non-rendered or non-processed. Adjusting the resolution of objects that are out-of-frame from a viewer ensures better performance for real-time applications since render engines often process geometry even when not seen [46] . Nanite also works to improve rendering by using a system of virtualized textures also automatically generated [47] . All of these optimizations are done by the system, removing the technical obstacles that could impact performance and allowing the developer to focus on viewer experience. This system, and others that are sure to follow, entirely removes many of the most time-consuming and technically challenging aspects of 3D asset creation.