Learning semantical dynamics and spatiotemporal collaboration for human pose estimation in video

TL;DR

This paper introduces a novel multi-level semantic and spatio-temporal framework for human pose estimation in videos, overcoming limitations of pixel-level methods by capturing semantic correlations and enhancing feature collaboration.

Contribution

It proposes a Multi-Level Semantic Motion Encoder and a Spatial-Motion Mutual Learning module to better model semantic dynamics and feature integration in video pose estimation.

Findings

Achieves state-of-the-art results on PoseTrack datasets.

Effectively models semantic relationships across frames.

Enhances robustness to occlusions and image quality issues.

Abstract

Temporal modeling and spatio-temporal collaboration are pivotal techniques for video-based human pose estimation. Most state-of-the-art methods adopt optical flow or temporal difference, learning local visual content correspondence across frames at the pixel level, to capture motion dynamics. However, such a paradigm essentially relies on localized pixel-to-pixel similarity, which neglects the semantical correlations among frames and is vulnerable to image quality degradations (e.g. occlusions or blur). Moreover, existing approaches often combine motion and spatial (appearance) features via simple concatenation or summation, leading to practical challenges in fully leveraging these distinct modalities. In this paper, we present a novel framework that learns multi-level semantical dynamics and dense spatio-temporal collaboration for multi-frame human pose estimation. Specifically, we first design a Multi-Level Semantic Motion Encoder using a multi-masked context and pose reconstruction strategy. This strategy stimulates the model to explore multi-granularity spatiotemporal semantic relationships among frames by progressively masking the features of (patch) cubes and frames. We further introduce a Spatial-Motion Mutual Learning module which densely propagates and consolidates context information from spatial and motion features to enhance the capability of the model. Extensive experiments demonstrate that our approach sets new state-of-the-art results on three benchmark datasets, PoseTrack2017, PoseTrack2018, and PoseTrack21.