Frostbite Master Thesis

Frostbite is EA's proprietary game engine that comes equipped with tons of tools and technologies designed to build incredible games. Our in-house engine offers the unique ability to adapt to each game while working hand-in-hand to prioritize what to build. Our goal is to empower creators all over the world to bring their best ideas to life by creating an engine that amplifies innovation across every discipline. Learn More

We are looking for Master Thesis students to work on our team and go deep in any of multiple problem spaces. For this current hiring cycle, we are looking for you who is interested in exploring any of the following subjects:

• Diffuse specular reflections with spherical gaussians

Real-time Diffuse specular reflections can be achieved with existing GI technology but breaks down in real world scenarios due to performance and storage complexity. Investigate different storage mediums in terms of efficiency, quality and performance.

• Crowd Ray Tracing

Crowd presents a non-trivial challenge when trying to ray trace against it efficiently, especially for 2-level hierarchies. These include: number of instances, sharing blas data, generating skinned vertex positions, etc. Investigate alternative strategies for tracing rays against crowds.

• Strand hair ray tracing

Strand hair is another challenging ray tracing topic. Including each strand individually into the TLAS is very expensive. Investigate strategies and alternative representations for ray tracing.

• Global Illumination Resampling

Interactive Global Illumination is still a computationally challenging problem. Recent advances in interactive GI has paved the way to use existing GI algorithms to augment unidirectional path tracing for screen-space reservoir resampling with a set of sparse world-space probes. Advances in this area could pave the wave for faster and more interactive GI in game titles.

• Real-time neural radiance caching for path tracing - Neural Radiance Cache, Spatial Hash Radiance Cache

The Neural Radiance Cache is an AI technique aimed at improving signal quality and potentially performance in the context of path tracing. The NRC operates in world space and predicts radiance at any point in the virtual world using path-traced live-trained data.

Spatial Hash Radiance Cache (SHaRC) is a similar technique aimed at achieving the same goals as NRC. Either technique potentially improves interactive GI massively. Investigate and draw conclusions on how these methods affect quality.

• Random-Access Neural Compression of Material

The continuous advancement of photorealism in rendering is accompanied by a growth in texture data and, consequently, increasing storage and memory demands. Novel research proposes an efficient way of storing information specifically designed for material textures, and unlocking more detail. Investigate the potential benefits of similar techniques.

Requirements

• You are pursuing a MsC. in Computer Science or a related field in Sweden.
• Experience with C++
• Graphics programming (Rendering)

This internship is unpaid and temporary employment. It needs to be associated with a university education or any other type of education that has an internship period planned into the education plan, for example LiA. We are not able to provide relocation services for this role, so we will only consider candidates already residing in Sweden.

We look forward to receiving your application