AI in Visual Effects and the Rise of NeRFs

Neural Radiance Fields (NeRFs) have the power to adapt the VFX industry significantly, particularly in the realm of AI VFX and 3D modelling. With NeRFs gaining prominence, the industry is witnessing a groundbreaking shift in how realistic 3D models are created. By harnessing the power of Luma AI and Nvidia Neural Radiance Fields, VFX artists can generate incredibly detailed and lifelike 3D representations. These NeRFs VFX offer an alternative on traditional modeling techniques and open up new possibilities for photorealistic visual effects.

NeRFs excel in handling scenes with complicated geometry and appearance, thanks to their effective optimisation and rendering capabilities. By optimising neural radiance fields, VFX artists can render novel views of scenes that exhibit realistic lighting and intricate details. The results achieved by NeRFs outperform prior works on neural rendering and view synthesis, elevating the quality and realism of VFX productions.

The integration of NeRFs with other AI techniques and software such as Luma AI, allow for complete AI-generated textures and dynamic simulations, further enhancing the versatility and potential of this technology. With the continuous advancements in NeRFs and their applications in VFX, the future holds promising opportunities for pushing the boundaries of realism and creativity in 3D modelling and visual effects.

NeRFs have been optimised by researchers (and special mention) Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi and Ren Ng. Their White paper is referenced at the bottom of the page.

They have found the means to generate detailed 3D models from 2D images, leveraging neural networks and deep learning techniques. The underlying concept of NeRFs revolves around the relationship between images and their corresponding 3D geometry.

The computing process of NeRFs involves training a neural network to model the volumetric representation of a scene. This process requires a significant amount of data, including images and corresponding 3D geometry. The neural network learns to reconstruct the 3D structure and appearance of the scene by optimising its parameters. The optimisation aims to minimise the difference between the rendered views and the input images, gradually improving the accuracy and fidelity of the NeRFs model.

Previous volumetric techniques for creating new views of scenes have limitations when it comes to handling higher-resolution images because they require a lot of computational resources. they have addressed this issue by using a deep neural network to represent the scene as a continuous volume. This approach not only produces better-quality renderings compared to previous methods but also requires much less storage space.

Neural Radiance Field Scene Representation – Image Source: Matthew Tancik

NeRFs use ray marching as a key technique to generate the volumetric representation of a scene. By casting rays from the camera through the scene and integrating the radiance along the ray, NeRFs calculate the colour and density at each point.

This network takes a single continuous 5D coordinate, which includes the spatial location (x, y, z) and viewing direction (θ, φ), as input. It then outputs the volume density and view-dependent emitted radiance at that specific spatial location. This representation allows NeRFs to reconstruct the 3D geometry and appearance of the scene with remarkable accuracy also accounting for occlusion, lighting variations, and complex material properties, resulting in highly detailed and realistic NeRFs models.

The continuous advancements in the computing process of NeRFs contribute to the ever-increasing potential and application of this technology in VFX. The ability to generate realistic lighting, intricate details, and accurate 3D representations opens up new possibilities for creating captivating visual effects and pushing the boundaries of realism in VFX productions.

Scalability:

One area of future advancement lies in scalability. Efforts are being made to optimise NeRF algorithms to handle larger and more complex scenes. This scalability will allow VFX artists to create NeRFs models of entire environments, such as sprawling cityscapes or vast landscapes, with the same level of detail and realism as smaller-scale models.

Dynamic Scenes:

Ongoing research focuses on the integration of dynamic elements into NeRFs. Currently, NeRFs predominantly represent static scenes. However, researchers are exploring techniques to incorporate dynamic objects and animations into NeRFs, enabling the creation of interactive and evolving scenes. This would open up new possibilities for creating dynamic visual effects and interactive narratives.

AI Integration:

Another area of exploration is the integration of NeRFs with AI-driven simulation techniques. By combining NeRFs with AI algorithms for simulating phenomena such as realistic lighting effects, fluid dynamics, and other physical interactions, VFX artists can enhance the visual fidelity and believability of NeRFs models. This integration holds promise for creating even more immersive and realistic virtual environments.

New NeRF Models

The initial NeRF model had certain drawbacks, such as slow training and rendering speeds, and its applicability was limited to static scenes only. Furthermore, it lacked flexibility as a NeRF model trained on one scene couldn’t be effectively utilised for other scenes.

To overcome these limitations, researchers have made notable advancements by developing various methods that build upon the NeRF framework. These models aim to address the aforementioned issues and enhance the overall capabilities of NeRF for a wider range of scenes and improved performance.

This video by Corridor Crew below gives a fun overview of NeRFs and their potential.