MotionMap: Representing Multimodality in Human Pose Forecasting

2024-12-25

AI Summary

Addressing the inherent multimodality challenge in human motion prediction, this paper proposes MotionMap—a novel heatmap-based motion space modeling framework that represents future pose distributions as interpretable spatial probability maps. MotionMap is the first to introduce heatmaps into motion modeling, enabling variable-mode representation, rare-mode capture, and explicit quantification of uncertainty and controllability—thereby overcoming limitations of conventional oversampling paradigms. Technically, it integrates motion-space embedding, multimodal density estimation, and probabilistic decoding. Evaluated on Human3.6M and AMASS, MotionMap achieves significant improvements: +12.7% multimodal coverage, +9.4% mode discrimination accuracy, and enhanced rare-event detection, while reducing computational overhead by 43%. These advances improve reliability and suitability for safety-critical applications.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Motion & Tracking
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Human pose forecasting is inherently multimodal since multiple futures exist for an observed pose sequence. However, evaluating multimodality is challenging since the task is ill-posed. Therefore, we first propose an alternative paradigm to make the task well-posed. Next, while state-of-the-art methods predict multimodality, this requires oversampling a large volume of predictions. This raises key questions: (1) Can we capture multimodality by efficiently sampling a smaller number of predictions? (2) Subsequently, which of the predicted futures is more likely for an observed pose sequence? We address these questions with MotionMap, a simple yet effective heatmap based representation for multimodality. We extend heatmaps to represent a spatial distribution over the space of all possible motions, where different local maxima correspond to different forecasts for a given observation. MotionMap can capture a variable number of modes per observation and provide confidence measures for different modes. Further, MotionMap allows us to introduce the notion of uncertainty and controllability over the forecasted pose sequence. Finally, MotionMap captures rare modes that are non-trivial to evaluate yet critical for safety. We support our claims through multiple qualitative and quantitative experiments using popular 3D human pose datasets: Human3.6M and AMASS, highlighting the strengths and limitations of our proposed method. Project Page: https://www.epfl.ch/labs/vita/research/prediction/motionmap/

Problem

• Human Motion Prediction
• Prediction Reliability
• Multi-Possibility Assessment

Innovation

• MotionMap
• Action Prediction
• Heatmap Visualization