  Procedural Content Generation via Machine Learning (PCGML) faces a
significant hurdle that sets it apart from other fields, such as image or text
generation, which is limited annotated data. Many existing methods for
procedural level generation via machine learning require a secondary
representation besides level images. However, the current methods for obtaining
such representations are laborious and time-consuming, which contributes to
this problem. In this work, we aim to address this problem by utilizing
gameplay videos of two human-annotated games to develop a novel multi-tail
framework that learns to perform simultaneous level translation and generation.
The translation tail of our framework can convert gameplay video frames to an
equivalent secondary representation, while its generation tail can produce
novel level segments. Evaluation results and comparisons between our framework
and baselines suggest that combining the level generation and translation tasks
can lead to an overall improved performance regarding both tasks. This
represents a possible solution to limited annotated level data, and we
demonstrate the potential for future versions to generalize to unseen games.

Computer Vision and Pattern Recognition;Machine Learning