SMP: Reusable Score-Matching Motion Priors for Physics-Based Character Control

TL;DR

This paper introduces SMP, a reusable, task-agnostic motion prior based on diffusion models, enabling naturalistic character control without retraining for each new task or style.

Contribution

The authors propose a novel method using score distillation sampling with diffusion models to create modular, reusable motion priors for physics-based character control.

Findings

SMP achieves high-quality, naturalistic motions comparable to state-of-the-art methods.

SMP can be repurposed into various style-specific priors from a single large dataset.

SMP effectively composes different motion styles to generate new behaviors.

Abstract

Data-driven motion priors that can guide agents toward producing naturalistic behaviors play a pivotal role in creating life-like virtual characters. Adversarial imitation learning has been a highly effective method for learning motion priors from reference motion data. However, adversarial priors, with few exceptions, need to be retrained for each new controller, thereby limiting their reusability and necessitating the retention of the reference motion data when applied to downstream tasks. In this work, we present Score-Matching Motion Priors (SMP), which leverages pre-trained motion diffusion models and score distillation sampling (SDS) to create reusable task-agnostic motion priors. SMPs can be pre-trained on a motion dataset, independent of any control policy or task. Once trained, SMPs can be kept frozen and reused as general-purpose reward functions to train new policies to produce naturalistic behaviors for downstream tasks. We show that a general motion prior trained on large-scale datasets can be repurposed into a variety of style-specific priors. Furthermore, SMP can compose different styles to synthesize new styles not present in the original dataset. Our method can create reusable and modular motion priors that produce high-quality motions comparable to state-of-the-art adversarial imitation learning methods. In our experiments, we demonstrate the effectiveness of SMP across a diverse suite of control tasks with physically simulated humanoid characters. Video available at https://youtu.be/jBA2tWk6vzU