DiffPose: SpatioTemporal Diffusion Model for Video-Based Human Pose Estimation

TL;DR

DiffPose introduces a diffusion-based model for video human pose estimation that effectively leverages temporal information and keypoint-focused representations, achieving state-of-the-art results on multiple benchmarks.

Contribution

The paper presents DiffPose, a novel diffusion architecture with spatiotemporal learning and multi-scale feature interaction for improved video human pose estimation.

Findings

Sets new state-of-the-art on PoseTrack benchmarks.

Can combine multiple pose estimates for better accuracy.

Allows adjusting iterative steps for feature refinement without retraining.

Abstract

Denoising diffusion probabilistic models that were initially proposed for realistic image generation have recently shown success in various perception tasks (e.g., object detection and image segmentation) and are increasingly gaining attention in computer vision. However, extending such models to multi-frame human pose estimation is non-trivial due to the presence of the additional temporal dimension in videos. More importantly, learning representations that focus on keypoint regions is crucial for accurate localization of human joints. Nevertheless, the adaptation of the diffusion-based methods remains unclear on how to achieve such objective. In this paper, we present DiffPose, a novel diffusion architecture that formulates video-based human pose estimation as a conditional heatmap generation problem. First, to better leverage temporal information, we propose SpatioTemporal Representation Learner which aggregates visual evidences across frames and uses the resulting features in each denoising step as a condition. In addition, we present a mechanism called Lookup-based MultiScale Feature Interaction that determines the correlations between local joints and global contexts across multiple scales. This mechanism generates delicate representations that focus on keypoint regions. Altogether, by extending diffusion models, we show two unique characteristics from DiffPose on pose estimation task: (i) the ability to combine multiple sets of pose estimates to improve prediction accuracy, particularly for challenging joints, and (ii) the ability to adjust the number of iterative steps for feature refinement without retraining the model. DiffPose sets new state-of-the-art results on three benchmarks: PoseTrack2017, PoseTrack2018, and PoseTrack21.