Narrow Results

Despite modern game systems adopting motion matching to retrieve an appropriate short motion clip from a database in real-time, existing methods struggle to support complex gaming scenes due to their inability to adapt live the motion retrieval based on the context. This paper presents the design and implementation of a context-aware character animation system, synthesizing realistic animations suitable for complex game scenes from a large-scale motion database. This system, called dynamic motion matching (DyMM), enables geometry and objects aware motion synthesis by introducing a two-phase context computation: an offline subspace decomposition of motion clips for creating a set of retrieval sub-spaces tailored to specific contexts and a subspace ensemble matching to compare relevant sub-features to determine the most appropriate motion clip. We also show the system architecture and implementation details applicable to a production-grade game engine. We verified the effectiveness of our method with industry-level motion data captured by professional game artists for multiple configurations and character controllers. The results of this study show that, by finding motion clips that comply well with the scene context, one can leverage large motion capture datasets to create practical systems that generate believable and controllable animations for games.