Activity: Neural Rendering
Neural rendering is a new method that combines computer graphics with machine learning (AI) to generate images. Instead of relying only on usual rendering techniques like geometry, materials, and lighting calculations, neural rendering uses trained models to predict how a scene should look. Generally, rendering pipelines are based on physically accurate simulations, which can be quite heavy and expensive. Neural rendering offers a different approach by learning visual patterns from data. Once trained, these systems can reproduce complex lighting, materials, and geometry with much lower computational cost in some cases. This is somewhat similar to AI Upscaling like DLSS 4-5, but a touch beyond.
One of the most well-known examples is Neural Radiance Fields (NeRFs). NeRFs can reconstruct a 3D scene from a set of 2D images by learning how light behaves at different points in space. Similar to Photogrammetry. Instead of storing geometry in a usual way, the scene is represented as a continuous function. This allows for a smooth switch between views and can produce highly realistic results.
Neural rendering is also used in real-time workflows. Similar techniques, such as AI Upscaling, denoising, and frame gen, are already integrated into modern pipelines. For example, AI denoisers can clean up noisy ray-traced images much faster than traditional methods, allowing for higher quality rendering in real time.
This can change how assets and scenes are created. Artists may no longer need to build everything manually, as some elements can be generated or enhanced through learned data. It also introduces new workflows where captured data, such as photographs or scans, can be turned directly into usable 3D content.
However, neural rendering also has the usual AI limitations. It can lack direct control compared to traditional methods, and the results depend heavily on the quality of the training data. It can also be difficult to edit or art-direct in the same way as standard geometry and materials.
Neural rendering might shift how content is produced. It does not replace traditional workflows, but it adds a new layer of tools that can speed up production and enable new forms of visual creation.
Source: https://www.matthewtancik.com/nerf
https://eureka.patsnap.com/article/how-neural-rendering-is-changing-the-future-of-cgi
https://www.microsoft.com/en-us/research/blog/renderformer-how-neural-networks-are-reshaping-3d-rendering/