Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision (1611.09813v5)

Abstract: We propose a CNN-based approach for 3D human body pose estimation from single RGB images that addresses the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data. Using only the existing 3D pose data and 2D pose data, we show state-of-the-art performance on established benchmarks through transfer of learned features, while also generalizing to in-the-wild scenes. We further introduce a new training set for human body pose estimation from monocular images of real humans that has the ground truth captured with a multi-camera marker-less motion capture system. It complements existing corpora with greater diversity in pose, human appearance, clothing, occlusion, and viewpoints, and enables an increased scope of augmentation. We also contribute a new benchmark that covers outdoor and indoor scenes, and demonstrate that our 3D pose dataset shows better in-the-wild performance than existing annotated data, which is further improved in conjunction with transfer learning from 2D pose data. All in all, we argue that the use of transfer learning of representations in tandem with algorithmic and data contributions is crucial for general 3D body pose estimation.

• The paper introduces innovative CNN supervision techniques and a new markerless dataset (X-3DHP) to boost generalization and accuracy in monocular 3D human pose estimation.
• It leverages multi-level corrective skip connections and skeletal joint relationships to achieve state-of-the-art performance on Human3.6m and X-3DHP test sets.
• The approach enables robust, single RGB image-based pose estimation applicable to real-world scenarios such as surveillance, sports analytics, and virtual reality.

Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision

The paper presented in the paper addresses the challenge of monocular 3D human pose estimation using Convolutional Neural Networks (CNNs) in uncontrolled environments. Traditional methods often rely on marker-based motion capture or multi-view setups, which restrict their applicability to controlled settings and specialized equipment. The proposed approach eliminates these constraints by leveraging single RGB images, making the solution accessible for diverse real-world scenarios.

Method Overview

The key contributions of the paper are twofold: 1) the introduction of novel CNN supervision techniques, and 2) the creation of a new dataset that enhances the generalizability of pose estimation models.

1. CNN Supervision Techniques: The paper proposes multi-level corrective skip connections and the use of parent and grandparent joint relationships in the skeletal kinematic tree. These architectural innovations aim to improve the learning and representation capabilities of the network.
2. New Dataset X-3DHP: To address the limitation of existing datasets, the authors introduce X-3DHP, a dataset captured using a marker-less multi-camera system. This dataset offers greater diversity in terms of human appearance, clothing, pose variety, and environmental settings (indoor and outdoor) compared to existing datasets.

Evaluation

The proposed model demonstrates significant performance improvements over existing benchmarks:

• Human3.6m: The method achieves state-of-the-art results with a mean per-joint position error (MPJPE) of approximately 74.11 mm. The incorporation of the X-3DHP dataset further reduces the error to 72.88 mm.
• X-3DHP Test Set: The model is evaluated on the newly introduced X-3DHP test set, which includes a variety of indoor and outdoor sequences. The results show that transfer learning from 2D to 3D pose estimation substantially improves generalizability, with 76.5% 3DPCK (Percentage of Correct Keypoints) on the test set when combining Human3.6m and X-3DHP datasets.

Strong Numerical Results

• Human3.6m Dataset: The paper details the significant numerical improvements across different activities, with the final model achieving 59.69 mm in Direct activity and 82.03 mm in Walk Dog activity.
• X-3DHP Test Set: The model attains 84.6% 3DPCK in green-screen settings and 72.4% in non-green-screen settings when both Human3.6m and X-3DHP datasets are used for training.

Implications and Future Developments

Practical Implications

1. Broader Applicability: The use of a single RGB camera makes the proposed method feasible for a wide array of applications, from surveillance and sports analytics to virtual reality and gaming.
2. Enhanced Realism: The dataset's diversity allows the model to generalize better to real-world images, which is crucial for applications requiring robust 3D pose estimation in unconstrained environments.

Theoretical Implications

1. Transfer Learning: The validated mechanism of feature transfer from 2D to 3D pose estimation sets a precedent for leveraging pre-trained 2D pose estimation models, thereby reducing the dependency on large annotated 3D datasets.
2. Multi-level Supervision: The proposed multi-level corrective skip connections and skeletal relationships provide new insights into CNN architecture design for pose estimation, suggesting that similar principles could be applied to other computer vision tasks.

Speculations on Future Developments in AI

1. Viewpoint Invariance: Future research could build on the proposed methods to tackle viewpoint elevation invariance more comprehensively, using the expanded version of the X-3DHP dataset with multiple camera elevations.
2. Temporal Smoothness: The integration with model-based temporal tracking methods may enhance temporal smoothness and accuracy for video sequences, addressing the current limitations of jitter in frame-by-frame predictions.
3. Real-time Processing: Optimizations aimed at reducing the model's runtime could facilitate real-time applications, expanding the practical utility of the proposed approach.

Conclusion

In summary, the paper's contributions through novel CNN supervision techniques and the introduction of the X-3DHP dataset represent a substantial advancement in monocular 3D human pose estimation. The proposed methods exhibit improved accuracy and generalization to in-the-wild conditions, setting a benchmark for future research in this domain.