Adversarially-Refined VQ-GAN with Dense Motion Tokenization for Spatio-Temporal Heatmaps

2025-09-23

AI Summary

To address the challenge of efficient compression and representation of high-dimensional, redundant continuous human motion data, this paper proposes a dense motion tokenization framework based on adversarially refined VQ-GAN. The method takes spatiotemporal heatmaps as input and integrates dense motion tokenization, adversarial training, and spatiotemporal encoding to explicitly model motion temporal structure, thereby mitigating motion blur and temporal misalignment. A key contribution is the novel revelation of the fundamental disparity in vocabulary size requirements between 2D and 3D action representations. The framework achieves high-fidelity motion reconstruction. On the CMU Panoptic dataset, it improves SSIM by 9.31% over dVAE and reduces temporal instability by 37.1%, demonstrating strong generalizability and deployment feasibility across downstream tasks—including action recognition, generation, and retrieval.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Continuous human motion understanding remains a core challenge in computer vision due to its high dimensionality and inherent redundancy. Efficient compression and representation are crucial for analyzing complex motion dynamics. In this work, we introduce an adversarially-refined VQ-GAN framework with dense motion tokenization for compressing spatio-temporal heatmaps while preserving the fine-grained traces of human motion. Our approach combines dense motion tokenization with adversarial refinement, which eliminates reconstruction artifacts like motion smearing and temporal misalignment observed in non-adversarial baselines. Our experiments on the CMU Panoptic dataset provide conclusive evidence of our method's superiority, outperforming the dVAE baseline by 9.31% SSIM and reducing temporal instability by 37.1%. Furthermore, our dense tokenization strategy enables a novel analysis of motion complexity, revealing that 2D motion can be optimally represented with a compact 128-token vocabulary, while 3D motion's complexity demands a much larger 1024-token codebook for faithful reconstruction. These results establish practical deployment feasibility across diverse motion analysis applications. The code base for this work is available at https://github.com/TeCSAR-UNCC/Pose-Quantization.

Problem

• Compressing high-dimensional human motion data while preserving fine-grained motion details
• Eliminating reconstruction artifacts like motion smearing and temporal misalignment
• Developing optimal tokenization strategies for 2D versus 3D motion complexity

Innovation

• Adversarially-refined VQ-GAN framework for motion compression
• Dense motion tokenization to eliminate reconstruction artifacts
• Compact token vocabulary analysis for 2D and 3D motion