Environment-aware Motion Matching

Abstract

Interactive applications demand believable characters that respond naturally to dynamic environments. Traditional character animation techniques often struggle to handle arbitrary situations, leading to a growing trend of dynamically selecting motion-captured animations based on predefined features. While Motion Matching has proven effective for locomotion by aligning to target trajectories, animating environment interactions and crowd behaviors remains challenging due to the need to consider surrounding elements. Existing approaches often involve manual setup or lack the naturalism of motion capture. Furthermore, in crowd animation, body animation is frequently treated as a separate process from trajectory planning, leading to inconsistencies between body pose and root motion. To address these limitations, we present Environment-aware Motion Matching, a novel real-time system for full-body character animation that dynamically adapts to obstacles and other agents, emphasizing the bidirectional relationship between pose and trajectory. In a preprocessing step, we extract shape, pose, and trajectory features from a motion capture database. At runtime, we perform an efficient search that matches user input and current pose while penalizing collisions with a dynamic environment. Our method allows characters to naturally adjust their pose and trajectory to navigate crowded scenes.

Published in ACM TOG and presented in SIGGRAPH ASIA 2025. Project webpage: https://upc-virvig.github.io/Environment-aware-Motion-Matching/

References in corpus (9)

• DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
• AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control
• Character Controllers Using Motion VAEs
• ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
• Combining Motion Matching and Orientation Prediction to Animate Avatars for Consumer-Grade VR Devices
• C$\cdot$ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters
• AdaptNet: Policy Adaptation for Physics-Based Character Control
• Generating Upper-Body Motion for Real-Time Characters Making their Way through Dynamic Environments
• PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers

Cited by in corpus (1)

• STyMo: Fast and Controllable Few-Shot Motion Style Transfer