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Machine Learning Summit: Advanced Heightmap Compression Using Deep Learning in 'Dune: Awakening'

Clemens Rögner  (Senior R&D Programmer, Tencent Games)

Calle Lejdfors  (Director of Production R&D, Tencent Games)

Location: Room 2010, West Hall

Date: Tuesday, March 21

Time: 1:20 pm - 1:50 pm

Pass Type: All Access Pass, Summits Pass

Topic: Programming

Format: Lecture

Vault Recording: Video

Audience Level: All

The vast desert landscapes of Arrakis are the key feature of Dune: Awakening. The players are confronted with dangerous environments and surviving in the harshness of the world. The large scale and its importance for the gameplay poses various challenges for the developers, not least of which is the landscape's enormous scale and the substantial on-disk size of the game.

To tackle the challenge of such large install sizes of terrain data, the team developed a novel approach using Deep Learning models to compress and decompress heightmap textures—which will be discussed in this session. With their lossy compression, the team was able to achieve compression rates ranging from 8x to 18x, satisfying critical games requirements for low storage space. They also kept the quality loss low and free of obvious artifacts, so the in-game experience did not suffer.


Attendees will leave with a good understanding of how to make machine learning models run in real-time on the CPU within a game, as well as the pitfalls and difficulties of implementing the machine learning model execution in the Unreal Engine on PC and Consoles.

Intended Audience

This is for anyone interested in using machine learning models at run-time in game development and has knowledge of programming and/or machine learning.