Deadline:
As soon as possible
Location(s)
France
Bordeaux
Overview
Details
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.
Opportunity is About
Eligibility
Candidates should be from:
Description of Ideal Candidate
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.
Dates
Deadline: As soon as possible
Cost/funding for participants
Un-save Opportunity
Internships, scholarships, student conferences and competitions.
