Intern - Research

Job Description

Research Internship (F/M/NB) - Neural Textures for Complex Materials - La Forge

About Ubisoft

Ubisoft's 19,000 team members, working across more than 30 countries around the world, are bound by a common mission to enrich players' lives with original and memorable gaming experiences. Their commitment and talent have brought to life many acclaimed franchises such as Assassin's Creed, Far Cry, Watch Dogs, Just Dance, Rainbow Six, and many more to come. Ubisoft is an equal opportunity employer that believes diverse backgrounds and perspectives are key to creating worlds where both players and teams can thrive and express themselves. If you are excited about solving game-changing challenges, cutting edge technologies and pushing the boundaries of entertainment, we invite you to join our journey and help us create the unknown.

Ubisoft Bordeaux

Founded in 2017, Ubisoft Bordeaux works with passion on the biggest AAA titles to deliver the best gaming experiences. Today, the studio is composed of 400 talents from 20 different nationalities, working on licenses such as Assassin's Creed, Beyond Good & Evil 2 and a free to play game, BattleCore Arena. At the same time, the studio has set up a Tech branch which works on all Ubisoft's online services (named Online Services) as well as on the Anvil game engine. Ubisoft Bordeaux is also home to a R&D team, La Forge, which brings together engineers and researchers to work together on prototypes for game production, particularly around AI topics.

Job Description

Textures provide critical surface details, but high-resolution textures for multiple material properties (e.g., diffuse color, normal maps) create a storage and performance bottleneck. Block Compression (BC) [1, 2, 3] formats are commonly used to reduce texture size, but they are not suited to handle high-dimensional data and don't scale well with increasing resolution.

Recent work [4, 5] has shown the potential of neural networks to model and represent material properties. This neural approach aims at replacing traditional textures with a collection of learned latent features, also known as neural textures, alongside a neural network. In this context, the network plays a crucial role in decoding the learned information and reconstructing the original material

Vaidyanathan et al. [6] have demonstrated how to use these neural representations to improve the compression rate of high-resolution material textures. However, their technique is not suited for real-time applications due to its reliance on a large decoder network and, most importantly, performing post-inference filtering.

At Ubisoft, we have developed a neural representation [7] for material textures that addresses these challenges. Our method uses a lightweight neural decoder with minimal computational overhead, enabling faster inference on GPUs. Additionally, we integrate filtering directly into the neural process, reducing extra computation. While our approach delivers scalable, high-quality texture compression and outperforms traditional BC methods in real-time environments, it currently supports only simple materials with a single texture set.

Extending this technique to efficiently handle complex materials with multiple texture sets is the focus of this internship, paving the way for broader adoption.

References

[1] https://en.wikipedia.org/wiki/S3_Texture_Compression

[2] https://www.reedbeta.com/blog/understanding-bcn-texture-compression-formats/

[3] Campbell, Graham, et al., ''Two bit/pixel full color encoding.'' ACM SIGGRAPH (1986).

[4] T. Zeltner et al., ''Real-Time Neural Appearance Models''. ACM SIGGRAPH (2024).

[5] A. Kuznetsov et al., ''Rendering Neural Materials on Curved Surfaces''. ACM SIGGRAPH (2022).

[6] K. Vaidyanathan, et al., ''Random-Access Neural Compression of Material Textures'', ACM SIGGRAPH (2023).

[7] C. Weinreich, et al., ''Real-Time Neural Materials using Block-Compressed Features''. Eurographics (2024).

Qualifications
• Currently a second-year master's student or a third-year engineering student.
• Solid foundation in Machine Learning, linear algebra, and signal processing.
• Knowledge of computer graphics fundamentals, such as texture sampling/filtering and shading is a plus.
• Proficiency in Python, and familiar deep learning frameworks (e.g., PyTorch, TensorFlow).
• Proficient in English, both written and spoken, with the ability to clearly communicate technical concepts and collaborate effectively with an international team.

Skills and competencies show up in different forms and can be based on different experiences, that's why we strongly encourage you to apply even though you may not have all the requirements listed above.

Jobcode: Reference SBJ-d8kev6-13-58-205-132-42 in your application.