MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video (2203.00859v4)

Abstract: Recent transformer-based solutions have been introduced to estimate 3D human pose from 2D keypoint sequence by considering body joints among all frames globally to learn spatio-temporal correlation. We observe that the motions of different joints differ significantly. However, the previous methods cannot efficiently model the solid inter-frame correspondence of each joint, leading to insufficient learning of spatial-temporal correlation. We propose MixSTE (Mixed Spatio-Temporal Encoder), which has a temporal transformer block to separately model the temporal motion of each joint and a spatial transformer block to learn inter-joint spatial correlation. These two blocks are utilized alternately to obtain better spatio-temporal feature encoding. In addition, the network output is extended from the central frame to entire frames of the input video, thereby improving the coherence between the input and output sequences. Extensive experiments are conducted on three benchmarks (Human3.6M, MPI-INF-3DHP, and HumanEva). The results show that our model outperforms the state-of-the-art approach by 10.9% P-MPJPE and 7.6% MPJPE. The code is available at https://github.com/JinluZhang1126/MixSTE.

• The paper introduces MixSTE, a seq2seq model that alternates spatial and temporal transformer blocks to enhance 3D human pose estimation.
• The model refines temporal dynamics and spatial relationships across entire video sequences, reducing MPJPE error by 7.6% on key benchmarks.
• Its versatile architecture streamlines inference and supports real-time applications in areas like virtual reality and surveillance.

Exploring MixSTE: Advancements in 3D Human Pose Estimation

The paper "MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video" presents a novel approach to improve the accuracy and efficiency of 3D human pose estimation from video sequences. This paper introduces MixSTE, a framework that transforms the sequential processing of 3D human poses by utilizing a mixed spatio-temporal encoder within a seq2seq architecture. By refining the modeling of temporal motion and spatial relationships in body joints, this work enhances both the coherence and accuracy of pose estimation.

Methodology

The primary innovation of MixSTE is the integration of transformer-based spatio-temporal encoding, wherein the spatial transformer block (STB) and the temporal transformer block (TTB) are alternately used. This design enhances the model's ability to separately capture temporal dynamics for each joint while concurrently learning inter-joint relationships. This dual approach allows for more nuanced modeling of the spatio-temporal correlations, addressing previous limitations in capturing the motion dynamics of individual joints.

One of the significant strengths of the MixSTE model is its seq2seq framework, which outputs 3D poses for complete sequences rather than focusing solely on the central frame, as seen in many preceding methods. This advancement facilitates a more efficient inference process by reducing redundant calculations and improving sequence coherence in the predicted 3D poses.

Results and Performance

Through extensive experiments on popular benchmarks such as Human3.6M, MPI-INF-3DHP, and HumanEva, MixSTE demonstrates superior performance over existing state-of-the-art methods. The model achieves a notable reduction in error rates, improving MPJPE by 7.6% compared to leading approaches. This improvement is consistent across diverse evaluation metrics including PCK, AUC, and MPJVE, indicating enhanced smoothness and stability in the predicted sequences.

The effectiveness of MixSTE is further underscored by its adaptability to variations in input sequence length, enabling robust performance across different testing conditions. Additionally, the use of a weighted mean per-joint position error (WMPJPE) and a combination of temporal loss components within the loss function effectively balances accuracy with temporal smoothness, resulting in more realistic pose estimations.

Implications and Future Directions

The introduction of MixSTE represents a significant methodological advance in the domain of 3D human pose estimation. By effectively leveraging transformer-based architectures to separately model spatial and temporal dynamics, this work opens new avenues for enhancing pose estimation accuracy in dynamic sequences. The seq2seq pipeline incorporated into MixSTE could inspire future models to explore more holistic approaches that capitalize on sequence-wide prediction strategies.

Practically, this research could impact areas such as virtual reality, where accurate real-time pose estimation is critical, or in surveillance systems that require fast and precise human pose tracking. Furthermore, it lays a foundational basis for further research into utilizing transformer-based models to manage other challenges in spatial-temporal data, particularly in handling noisy or incomplete input keypoints.

In summary, the MixSTE model advances the current capabilities in the field of 3D human pose estimation, offering a compelling framework for academia and industry researchers striving to push the boundaries of human motion analysis and synthesis. As AI continues to evolve, methodologies like MixSTE will be crucial in bridging the gap between theoretical models and real-world applications.