A generalizable approach for multi-view 3D human pose regression (1804.10462v2)

Abstract: Despite the significant improvement in the performance of monocular pose estimation approaches and their ability to generalize to unseen environments, multi-view (MV) approaches are often lagging behind in terms of accuracy and are specific to certain datasets. This is mainly due to the fact that (1) contrary to real world single-view (SV) datasets, MV datasets are often captured in controlled environments to collect precise 3D annotations, which do not cover all real world challenges, and (2) the model parameters are learned for specific camera setups. To alleviate these problems, we propose a two-stage approach to detect and estimate 3D human poses, which separates SV pose detection from MV 3D pose estimation. This separation enables us to utilize each dataset for the right task, i.e. SV datasets for constructing robust pose detection models and MV datasets for constructing precise MV 3D regression models. In addition, our 3D regression approach only requires 3D pose data and its projections to the views for building the model, hence removing the need for collecting annotated data from the test setup. Our approach can therefore be easily generalized to a new environment by simply projecting 3D poses into 2D during training according to the camera setup used at test time. As 2D poses are collected at test time using a SV pose detector, which might generate inaccurate detections, we model its characteristics and incorporate this information during training. We demonstrate that incorporating the detector's characteristics is important to build a robust 3D regression model and that the resulting regression model generalizes well to new MV environments. Our evaluation results show that our approach achieves competitive results on the Human3.6M dataset and significantly improves results on a MV clinical dataset that is the first MV dataset generated from live surgery recordings.

• The paper proposes a generalizable two-stage method for multi-view 3D human pose estimation, separating 2D detection and 3D regression.
• This approach leverages diverse single-view and multi-view datasets by generating training data via projection, avoiding the need for environment-specific annotations.
• Empirical evaluations show significant improvements in 3D pose estimation accuracy and strong generalization capabilities across diverse environments, enabling practical applications.

Generalizable Multi-view 3D Human Pose Estimation

This paper addresses the limitations of current multi-view 3D human pose estimation approaches, offering a two-stage method to improve generalizability and accuracy in real-world applications. The proposed method separates single-view 2D pose detection from multi-view 3D pose estimation. This division leverages the strengths of both single-view and multi-view datasets to enhance model robustness and precision.

Methodology

The researchers introduce a novel framework where detection and estimation tasks are distinct. The initial stage involves using a state-of-the-art single-view 2D pose detector to identify human poses in individual camera views independently. The second stage employs a multi-stage neural network that processes 2D detections across multiple views to estimate the 3D body positions. Crucially, this approach avoids the need for newly annotated data from specific camera setups by generating training data through projection of 3D poses into 2D.

The separation into two tasks allows the use of existing single-view datasets, such as MS COCO and MPII Pose, for constructing robust models capable of handling real-world challenges like occlusion and clutter. These datasets provide diverse examples to train the detection model against complex environments. For the regression model, multi-view datasets provide accurate 3D annotations necessary for precise estimation.

Results and Evaluation

Empirical evaluations demonstrate that this method achieves substantial improvements in 3D pose estimation accuracy across diverse environments. The approach was tested on the Human3.6M dataset, a controlled multi-view dataset, showing competitive results, and it significantly reduced errors when applied to a multi-view dataset derived from real surgical environments (MVOR). The ability to generalize to new environments without needing retrained data showcases the strength of the method, achieving competitive results against models trained on environment-specific datasets.

Implications and Future Work

This research presents practical implications for deploying 3D pose estimation models in various fields, including augmented reality, human-computer interaction, and surveillance systems. The approach could streamline workflows requiring human pose data by overcoming the constraints of controlled environments. Moreover, it demonstrates robust model adaptability given varying camera setups, providing possibilities for easily deploying the model in new settings with different camera orientations and configurations.

Future research could explore further enhancements in the training data generation process, perhaps by incorporating realistic noise models to simulate complex detection scenarios. Another research direction could include integrating temporal information, improving model robustness in dynamic or rapidly changing environments. Additionally, exploring the application of this approach to non-static environments, such as moving camera systems, may further broaden its applicability and enhance its utility in real-time systems.