Capturing Hands in Action using Discriminative Salient Points and Physics Simulation (1506.02178v4)

Abstract: Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.

• The paper presents a framework that integrates generative and discriminative models with physics simulation to achieve realistic hand motion capture.
• It leverages deep learning-based fingertip detection, collision handling, and a Gauss-Newton optimization to outperform prior pose estimation methods.
• The method accurately tracks challenging scenarios with occlusions and hand-object interactions, enabling applications in VR, robotics, and rehabilitation.

Overview of Hand Motion Capture Using Discriminative Salient Points and Physics Simulation

This paper presents a robust framework for capturing hand motion, introducing significant enhancements over previous efforts, particularly in scenarios involving interactions between hands or with objects. Traditional approaches often focus on isolated hand tracking, leaving the challenges of interactive scenarios under-addressed. The authors propose a sophisticated model combining generative and discriminative methodologies with physics-based simulation, resulting in more accurate and physically plausible hand tracking even in challenging environments with occlusions or lacking visual data.

Key Components and Methodology

The framework is rooted in a Linear Blend Skinning (LBS) model that defines a detailed mesh and kinematic skeleton for the hands and objects involved. The authors detail a comprehensive objective function that integrates multiple terms catering to different aspects of motion capture:

1. Data Alignment: Two terms focus on fitting the model to data and vice versa, ensuring a robust identification of correspondences in both RGB-D and multi-view RGB setups.
2. Salient Points: A discriminative element arises from employing a deep learning-based fingertip detection that aids in correcting the generative model when traditional tracking might fail.
3. Collision Detection: Self-intersections and unrealistic mesh penetrations get addressed through a collision detection mechanism, ensuring continuity and differentiability.
4. Physics Simulation: The introduction of a physics-based component enhances realism by simulating hand-object interactions, particularly useful when visual cues are ambiguous or missing.

The proposed approach optimizes this multi-term objective function using a Gauss-Newton method, demonstrating adaptability to complex scenarios.

Evaluation and Results

The evaluation of the proposed system was conducted extensively over 21 sequences with a monocular RGB-D camera and 8 sequences using synchronized multi-camera RGB systems. The challenges inherent in these sequences, such as severe hand occlusions and interacting with both rigid and articulated objects, underscore the robustness of the method. Notably, the combination of physics simulation and collision detection significantly contributes to achieving plausible hand poses in scenarios where purely visual cues fail.

Quantitative results are achieved through careful comparisons with state-of-the-art methods, such as the Particle Swarm Optimization approach by Oikonomidis et al. The proposed framework consistently presents superior accuracy in pose estimation for both RGB-D and RGB conditions. The introduction of annotated datasets further strengthens this evaluation, providing ground-truth data for performance validation.

Implications and Future Directions

The implications of this research are multifaceted. From practical perspectives, the framework is well-suited for applications in robotics, virtual reality, and rehabilitation, where understanding hand interactions with diverse environments is critical. Theoretically, it opens avenues for further exploration into robust model-data fusion techniques and better handling of partial observability in motion capture.

Future research may focus on enhancing runtime efficiency, potentially integrating real-time feedback loops to inform the physics component dynamically. Another promising direction would be extending the physics model's complexity to cover more intricately articulated interactions and behaviors.

In conclusion, this research contributes significantly to the domain of motion capture, moving beyond isolated hand tracking to yield a system that can accurately and realistically track hand interactions in diverse and complex scenarios. This comprehensive approach sets a foundation for future advancements in both algorithmic efficiency and application in varied interactive environments.