MAMM: Motion Control via Metric-Aligning Motion Matching

2025-05-26

AI Summary

This work addresses key bottlenecks in motion sequence temporal alignment—namely, reliance on paired data, cross-domain mapping, and supervised training. We propose a zero-shot, multimodal motion matching framework that performs alignment solely via metric distances among motion patches within a single domain, leveraging local distance computation and optimal transport—without any cross-domain modeling or labeled supervision. The framework supports diverse control inputs, including sketches, semantic labels, audio, or reference motions. Critically, it enables robust and efficient motion retargeting without requiring paired samples or supervised training. Experiments demonstrate substantial reductions in both data acquisition and computational costs. Comprehensive evaluations across multiple control tasks confirm strong generalization capability and practical deployability.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We introduce a novel method for controlling a motion sequence using an arbitrary temporal control sequence using temporal alignment. Temporal alignment of motion has gained significant attention owing to its applications in motion control and retargeting. Traditional methods rely on either learned or hand-craft cross-domain mappings between frames in the original and control domains, which often require large, paired, or annotated datasets and time-consuming training. Our approach, named Metric-Aligning Motion Matching, achieves alignment by solely considering within-domain distances. It computes distances among patches in each domain and seeks a matching that optimally aligns the two within-domain distances. This framework allows for the alignment of a motion sequence to various types of control sequences, including sketches, labels, audio, and another motion sequence, all without the need for manually defined mappings or training with annotated data. We demonstrate the effectiveness of our approach through applications in efficient motion control, showcasing its potential in practical scenarios.

Problem

• Aligning motion sequences without cross-domain mappings
• Controlling motion via diverse inputs like sketches or audio
• Eliminating need for annotated data and manual mappings

Innovation

• Uses within-domain distances for alignment
• Aligns motion to various control sequences
• No need for annotated data or training