DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation

Abstract: This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch. Unlike current solutions that estimate each frame in a video, DeciWatch introduces a simple yet effective sample-denoise-recover framework that only watches sparsely sampled frames, taking advantage of the continuity of human motions and the lightweight pose representation. Specifically, DeciWatch uniformly samples less than 10% video frames for detailed estimation, denoises the estimated 2D/3D poses with an efficient Transformer architecture, and then accurately recovers the rest of the frames using another Transformer-based network. Comprehensive experimental results on three video-based human pose estimation and body mesh recovery tasks with four datasets validate the efficiency and effectiveness of DeciWatch. Code is available at https://github.com/cure-lab/DeciWatch.

• The paper introduces DeciWatch, a framework that achieves 10x efficiency in video-based 2D and 3D pose estimation using sparse frame sampling.
• It employs a three-step process—sampling minimal frames, denoising with Transformer architectures, and recovering missing frames—to lower computational load.
• Experimental results on several datasets demonstrate robust performance, making DeciWatch suitable for resource-constrained and real-time applications.

DeciWatch: Enhanced Efficiency in Video-Based Human Pose Estimation

The paper presents a novel framework, DeciWatch, designed to elevate the efficiency of video-based 2D and 3D human pose estimation by approximately tenfold without compromising performance. This is realized through a sample-denoise-recover methodology that smartly minimizes the computation load, leveraging the inherent continuity in human motion.

Key Contributions and Methodology

DeciWatch departs from traditional approaches that estimate poses for every frame, introducing an efficient framework that processes only a sparse selection of frames. It employs a three-step process:

1. Sampling Minimal Frames: Less than 10% of video frames are sampled uniformly for detailed estimation, exploiting the redundancy in successive frames and the continuity in motion.
2. Denoising with Transformer Architectures: Following sampling, the poses are denoised using a Transformer-based approach, aptly named DenoiseNet. This mechanism purges noise from estimated 2D/3D poses, refining them for subsequent recovery.
3. Recovering Missing Frames: Another Transformer-based model, RecoverNet, leverages denoised keyframe data to infer the complete pose sequence. This phase crucially reconstructs continuous motion from the available sparse data.

Experimental Validation

The efficacy of DeciWatch was demonstrated across several diverse datasets, validating its robustness and precision. DeciWatch significantly enhances efficiency on tasks related to human pose estimation and body mesh recovery, surpassing state-of-the-art methods in computation speed without losing accuracy. Notably, experiments on datasets such as Sub-JHMDB, Human3.6M, 3DPW, and AIST++ illustrate its application across varying motion complexities, from surveillance to complex dance sequences.

Implications and Future Scope

DeciWatch's contributions are twofold: computational efficiency and improved pose sequence accuracy. Its design is such that it can integrate with multiple single-frame pose estimators, making it an adaptable and generalized solution. The outcomes suggest applications beyond current explorations, particularly in resource-constrained environments where lightweight computation is essential, such as smart cameras or mobile devices.

Looking ahead, the paper highlights potential courses for advancements, including the development of adaptive sampling strategies and dynamic recovery networks to further heighten efficiency. These innovations could drastically reduce computation cost while enhancing overall robustness and accuracy. Moreover, advancing these methods could open new avenues for leveraging multi-modal data, employing sensory data alongside visual data to attenuate computational requirements further.

Conclusion

DeciWatch sets a robust precedent in the field of video-based human pose estimation, proving that the integration of sampled frames, noise reduction, and intelligent reconstruction can yield substantial efficiency gains. The proposed framework not only broadens the horizon for more efficient pose estimation algorithms but also challenges prevailing assumptions about the necessity of processing every frame in pursuit of detailed human pose data. This approach heralds a pivot toward a more strategic, informed, and efficient era of computational pose estimation.