Using a single RGB frame for real time 3D hand pose estimation in the wild (1712.03866v1)

Abstract: We present a method for the real-time estimation of the full 3D pose of one or more human hands using a single commodity RGB camera. Recent work in the area has displayed impressive progress using RGBD input. However, since the introduction of RGBD sensors, there has been little progress for the case of monocular color input. We capitalize on the latest advancements of deep learning, combining them with the power of generative hand pose estimation techniques to achieve real-time monocular 3D hand pose estimation in unrestricted scenarios. More specifically, given an RGB image and the relevant camera calibration information, we employ a state-of-the-art detector to localize hands. Given a crop of a hand in the image, we run the pretrained network of OpenPose for hands to estimate the 2D location of hand joints. Finally, non-linear least-squares minimization fits a 3D model of the hand to the estimated 2D joint positions, recovering the 3D hand pose. Extensive experimental results provide comparison to the state of the art as well as qualitative assessment of the method in the wild.

• The paper introduces a novel approach that blends discriminative and generative methods for efficient 3D hand pose estimation using a single RGB frame.
• Key components include a retrained YOLO V2 detector achieving 92.8% accuracy for hand localization and OpenPose for reliable 2D joint estimation.
• Experimental results demonstrate superior real-world performance over baselines, paving the way for accessible AR/VR applications without depth sensors.

Using a Single RGB Frame for Real-Time 3D Hand Pose Estimation in the Wild

The paper authored by Panteleris, Oikonomidis, and Argyros presents a novel approach to real-time 3D hand pose estimation using a single RGB camera. The authors target a challenging aspect of pose estimation by eliminating the dependency on depth information which is common in many existing methodologies utilizing RGBD sensors. They aim to achieve robust 3D hand pose estimation in unconstrained environments by leveraging advances in deep learning and generative modeling.

Methodology

The proposed method consists of a sophisticated pipeline that seamlessly integrates discriminative and generative components to effectively solve the problem. The approach is divided into three primary phases:

1. Hand Detection: The authors utilize a retrained version of the YOLO V2 detector for hand localization. This CNN-based detector is both efficient and capable of real-time operation, which is imperative for their application. Through tailoring YOLO's architecture, a high detection rate of 92.8% with a low false positive rate was achieved.
2. 2D Joint Localization: For localizing the 2D hand joints, the method leverages the OpenPose architecture. This method is notable for its ability to handle occluded joints since OpenPose is trained on a multiview bootstrapped dataset, allowing for comprehensive joint estimation even in complex hand poses.
3. 3D Pose Estimation: The culmination of the approach is the transformation of 2D joint data into a 3D hand model. The authors apply a model fitting technique via non-linear least-squares optimization, utilizing a parametric hand model with defined joint limits. This generative method aligns the model to the detected 2D joint positions, resulting in an estimated 3D hand pose.

Experimental Results

The experimental validation is comprehensive, testing the method against both real-world and synthetic datasets. The authors compare their method with existing baselines, particularly that of Zimmermann and Brox, and demonstrate superior performance in real-world scenarios, attributable to lower abstraction levels in model estimation. Their method maintains a competitive edge in terms of both accuracy and processing speed.

The experimental outcome illustrates a significant improvement in the monocular estimation's efficacy with additional viewpoints, validating the adaptability of the method to multicamera setups.

Implications and Future Work

The implications of this research are multifaceted. It enables more accessible and flexible deployment of 3D hand pose estimation by utilizing ubiquitous RGB cameras. This can cater to Augmented Reality (AR) and Virtual Reality (VR) applications which prioritize natural interaction with minimal hardware constraints. Moreover, it reduces the environmental constraints associated with RGBD hardware, such as ambient lighting and effective depth range.

Future research directions proposed by the authors include refining the automatic adjustment of hand model parameters and enhancing the robustness of their setup by integrating color information and automatic camera calibration capabilities. This suggests a trajectory towards even more generalized and user-friendly solutions for real-time 3D hand tracking.

This work stands as a significant contribution to the computer vision and machine learning communities, advancing the state of monocular hand pose estimation and expanding the practical applications of these technologies.