Latent Dataset Distillation for Human Motion Prediction

2026-09-22

AI Summary

本文提出了一种潜变量数据蒸馏框架，通过学习运动先验来解决高维且结构耦合的人体运动预测问题，优于直接梯度匹配和随机子集方法。

Abstract

Dataset distillation (DD) compresses a large training set into a compact synthetic set while preserving downstream training utility. Although DD has been widely studied for images and recently extended to time-series forecasting, its application to human motion prediction remains largely unexplored. Human motion is high-dimensional and structurally coupled, and gradient matching (GM) in the original motion space optimizes many correlated variables without a prior on pose plausibility or temporal dynamics, which frequently yields implausible and unstable synthetic motions. To address this limitation, we propose a latent DD framework that regularizes distillation with a learned motion prior. Motions are first compressed by a residual-quantized variational autoencoder (RVQ-VAE), and distillation then updates only a learnable latent bank through the frozen quantizer and decoder. The pretrained decoder restricts synthetic motions to its output space, while residual quantization progressively refines the latent approximation across multiple codebooks and alleviates the representational bottleneck of single-stage vector quantization. Experiments on Human3.6M, CMU, and 3DPW with two prediction backbones show that the proposed framework outperforms direct GM in 27 of 30 evaluated settings and random subsets in every setting, and produces visibly more plausible synthetic motions in qualitative comparisons.

Problem

• Dataset Distillation
• Human Motion Prediction
• Gradient Matching

Innovation

• Latent Dataset Distillation
• Human Motion Prediction
• Residual-Quantized VAE
• Gradient Matching