Efficient Transformer-Based 3D Human Pose Estimation with Hourglass Tokenizer

Centrala begrepp

Efficiently prune and recover pose tokens to improve the efficiency of transformer-based 3D human pose estimation.

Sammanfattning

The article introduces the Hourglass Tokenizer (HoT) framework for efficient transformer-based 3D human pose estimation. It addresses the high computational costs of video pose transformers (VPTs) by pruning redundant pose tokens and recovering full-length tokens. The proposed method achieves high efficiency and estimation accuracy compared to existing VPT models through token pruning and token recovering strategies. Extensive experiments on benchmark datasets demonstrate the effectiveness of the HoT framework.

Directory:

1. Introduction
   • Video-based 3D human pose estimation applications.
   • Transformer-based architectures for video pose estimation.
2. Method
   • Token Pruning Cluster (TPC) for selecting representative tokens.
   • Token Recovering Attention (TRA) for restoring full-length tokens.
3. Experiments
   • Ablation studies on block index and number of representative tokens.
   • Comparison with state-of-the-art methods on Human3.6M and MPI-INF-3DHP datasets.
4. Conclusion
   • Summary of the proposed HoT framework for efficient 3D human pose estimation.

Statistik

Our HoT can save nearly 50% FLOPs without sacrificing accuracy and nearly 40% FLOPs with only 0.2% accuracy drop.
Extensive experiments on Human3.6M and MPI-INF-3DHP datasets demonstrate the efficiency and accuracy of our method.

Citat

"Our method reveals that maintaining the full pose sequence is unnecessary, and using a few pose tokens of representative frames can achieve both high efficiency and estimation accuracy."

Viktiga insikter från

Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation

by Wenhao Li,Me... på arxiv.org 03-28-2024

https://arxiv.org/pdf/2311.12028.pdf

Djupare frågor

How can the HoT framework be further optimized for real-time applications?

The HoT framework can be optimized for real-time applications by implementing parallel processing techniques to speed up the inference process. This can involve utilizing hardware accelerators like GPUs or TPUs to distribute the computational workload efficiently. Additionally, optimizing the token pruning and recovering algorithms for faster execution can significantly improve the real-time performance of the framework. Furthermore, exploring techniques like quantization and model compression to reduce the model size and computational complexity can also enhance the real-time capabilities of the HoT framework.

What are the potential limitations of the token pruning and recovering strategies proposed in the article?

One potential limitation of the token pruning strategy is the risk of losing important temporal information by discarding certain pose tokens. If the pruning process is not carefully designed, it may result in a loss of context and lead to suboptimal performance in 3D human pose estimation. Additionally, the token recovering strategy may introduce noise or artifacts during the reconstruction of full-length tokens, especially if the recovering mechanism is not robust enough to capture the intricate spatio-temporal relationships in the data. Balancing the trade-off between efficiency and accuracy in the token pruning and recovering processes is crucial to mitigate these limitations.

How might the insights from this study be applied to other fields beyond 3D human pose estimation?

The insights from this study on token pruning and recovering strategies can be applied to various other fields beyond 3D human pose estimation that involve sequential data processing. For instance, in video analysis tasks such as action recognition, gesture recognition, and activity detection, similar token pruning techniques can be employed to reduce computational costs while maintaining performance. In natural language processing tasks, token pruning and recovering strategies can be adapted for efficient text processing and sequence modeling. Additionally, in medical imaging applications like MRI or CT scan analysis, these strategies can help optimize the processing of volumetric data for faster and more accurate diagnostics. The principles of token pruning and recovering can be generalized to any domain that involves sequential data processing, offering opportunities for enhanced efficiency and performance.