Learning Monocular 3D Human Pose Estimation from Multi-view Images (1803.04775v2)

Abstract: Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves open the problem of capturing motions for which no such database exists. Manual annotation is tedious, slow, and error-prone. In this paper, we propose to replace most of the annotations by the use of multiple views, at training time only. Specifically, we train the system to predict the same pose in all views. Such a consistency constraint is necessary but not sufficient to predict accurate poses. We therefore complement it with a supervised loss aiming to predict the correct pose in a small set of labeled images, and with a regularization term that penalizes drift from initial predictions. Furthermore, we propose a method to estimate camera pose jointly with human pose, which lets us utilize multi-view footage where calibration is difficult, e.g., for pan-tilt or moving handheld cameras. We demonstrate the effectiveness of our approach on established benchmarks, as well as on a new Ski dataset with rotating cameras and expert ski motion, for which annotations are truly hard to obtain.

• The paper introduces a method using multi-view consistency as weak supervision to train monocular 3D human pose estimation networks, reducing reliance on extensive manual annotations.
• It achieves improved accuracy on benchmarks like Human3.6M and MPII-INF-3DHP, demonstrating practical effectiveness on a challenging Ski dataset with minimal annotations.
• This approach significantly lessens dependency on large annotated datasets, paving the way for more scalable and adaptable pose estimation systems in complex real-world scenarios.

Analysis of "Learning Monocular 3D Human Pose Estimation from Multi-view Images"

This paper addresses the challenging problem of accurately estimating 3D human poses from single images, particularly in scenarios where conventional datasets are insufficient or unavailable. The authors propose a method of leveraging multi-view images during training to mitigate the need for extensive manual annotations, facilitating the advancements in monocular 3D human pose estimation using deep neural networks.

Methodology

The paper introduces an innovative approach that employs multi-view consistency as a form of weak supervision. This is particularly beneficial when handling activities with limited training data, such as specific sports motions (e.g., skiing). The proposed method involves several components designed to enhance the accuracy of pose estimation:

1. Multi-view Consistency: The network is trained to predict consistent poses across multiple views, thus enforcing a weak supervision signal. This constraints are set up in such a way to ensure that the predicted poses in different camera angles remain congruent, even when extensive annotations are unavailable.
2. Supervised Loss and Regularization: To counteract the trivial solutions arising from the multi-view consistency constraint alone (such as predicting identical poses regardless of input), the authors include a supervised loss derived from a small set of labeled images. Additionally, a regularization term helps prevent divergence from initial pose predictions during training.
3. Joint Camera and Human Pose Estimation: The method extends to estimating the camera pose concurrently with the human pose, allowing the use of footage from scenarios with complicated calibration situations, like moving cameras.

Results and Improvements

The paper validates its methodology across various established benchmarks, such as Human3.6M and MPII-INF-3DHP datasets. It shows notable improvements in pose estimation accuracy even when the annotated data is scarce. Importantly, the approach also proves to be effective in real-world applications, demonstrated by its success on a new Ski dataset. This dataset involves challenging conditions with rotating cameras and dynamic ski motions, emphasizing the practical applicability of the method.

Implications and Future Directions

The significance of this research lies in its ability to lessen the dependency on large annotated datasets, thereby addressing a crucial bottleneck in the deployment of 3D human pose estimation systems at scale. The findings imply that leveraging weak supervision through multi-view images can significantly augment the robustness and generalization of deep-net models in monocular pose estimation tasks.

Looking forward, incorporating temporal consistency into the framework could further enhance performance, especially in sequential or video data. This would involve moving beyond single-frame predictions to models that account for motion dynamics across frames. Another intriguing direction could be exploring applications beyond human pose estimation, such as monitoring wildlife or other objects where labeled data is challenging to obtain.

This work provides a valuable contribution to the field of computer vision and 3D pose estimation by demonstrating a practical approach to reduce manual annotation labor without sacrificing accuracy, opening pathways for more generalized and adaptive pose estimation systems.