PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation

TL;DR

PoseFormerV2 introduces a frequency domain approach to enhance the efficiency and robustness of 3D human pose estimation, addressing computational and noise robustness issues in transformer-based methods.

Contribution

It leverages frequency domain representations to improve scalability and noise robustness with minimal changes to PoseFormer, achieving better speed-accuracy trade-offs.

Findings

Outperforms original PoseFormer on benchmark datasets.

Achieves better speed-accuracy trade-off.

Demonstrates robustness to noisy 2D joint detection.

Abstract

Recently, transformer-based methods have gained significant success in sequential 2D-to-3D lifting human pose estimation. As a pioneering work, PoseFormer captures spatial relations of human joints in each video frame and human dynamics across frames with cascaded transformer layers and has achieved impressive performance. However, in real scenarios, the performance of PoseFormer and its follow-ups is limited by two factors: (a) The length of the input joint sequence; (b) The quality of 2D joint detection. Existing methods typically apply self-attention to all frames of the input sequence, causing a huge computational burden when the frame number is increased to obtain advanced estimation accuracy, and they are not robust to noise naturally brought by the limited capability of 2D joint detectors. In this paper, we propose PoseFormerV2, which exploits a compact representation of lengthy skeleton sequences in the frequency domain to efficiently scale up the receptive field and boost robustness to noisy 2D joint detection. With minimum modifications to PoseFormer, the proposed method effectively fuses features both in the time domain and frequency domain, enjoying a better speed-accuracy trade-off than its precursor. Extensive experiments on two benchmark datasets (i.e., Human3.6M and MPI-INF-3DHP) demonstrate that the proposed approach significantly outperforms the original PoseFormer and other transformer-based variants. Code is released at \url{https://github.com/QitaoZhao/PoseFormerV2}.