In this lesson, you will learn how to optimize your Motion Matching databases to handle a wide variety of character rigs and movement speeds without sacrificing performance.
When scaling Motion Matching to support multiple character types—such as varying heights, limb lengths, or locomotion styles—using a single, monolithic animation database often leads to "pose snapping" or performance bottlenecks. To manage this effectively, we must decouple the animation data from the character rig and movement intensity.
Normalizing Animation Data
The secret to scaling is Data Normalization. Instead of storing raw world-space coordinates in your Motion Matching Database, you should utilize normalized trajectory and pose data. By expressing movement in local-space relative to the character’s root or pelvis, you ensure that the same animation clips can be reused across different character skeletons. This allows you to drive a small goblin and a large troll using the same foundational database, provided their movement patterns share similar logic.
Implementing Feature Channels for Speed
To handle varying movement speeds, we utilize Feature Channels within the Pose Search database. By weighting specific channels—such as Trajectory Velocity or Root Bone Velocity—you can tune how the system interprets input. For a high-speed character, you might increase the weight of the velocity channel to ensure the system prioritizes fast-paced, high-intensity animations:
// Conceptual adjustment of weightings for a high-speed character profile
PoseSearchDatabase->SetChannelWeight(EChannelType::TrajectoryVelocity, 2.0f);
PoseSearchDatabase->SetChannelWeight(EChannelType::PoseBoneRotation, 1.0f);
Performance Optimization at Scale
When dealing with multiple entities, memory management becomes critical. Rather than keeping every animation sequence loaded, use Animation Data Tags to partition your database. By dynamically loading only the segments of the database relevant to a character's current state (e.g., "Combat" vs. "Exploration" tags), you keep the search space small. This ensures that even with 50 characters on screen, the cost of searching for the optimal pose remains constant, preventing frame spikes and maintaining high performance.
Key Takeaway: Scaling Motion Matching relies on normalizing your animation data for rig independence and utilizing data tagging to minimize the search space, ensuring consistent performance regardless of character type or entity count.