How much can we infer about human shape using an event camera that only detects the pixel position where the luminance changed and its timestamp? This neuromorphic vision technology captures changes in pixel values at ultra-high speeds, regardless of the variations in environmental lighting brightness. Existing methods for human mesh recovery (HMR) from event data need to utilize intensity images captured with a generic frame-based camera, rendering them vulnerable to low-light conditions, energy/memory constraints, and privacy issues. In contrast, we explore the potential of solely utilizing event data to alleviate these issues and ascertain whether it offers adequate cues for HMR, as illustrated in Fig. 1. This is a quite challenging task due to the substantially limited information ensuing from the absence of intensity images. To this end, we propose EventPointMesh, a framework which treats event data as a three-dimensional (3D) spatio-temporal point cloud for reconstructing the human mesh. By employing a coarse-to-fine pose feature extraction strategy, we extract both global features and local features. The local features are derived by processing the spatio-temporally dispersed event points into groups associated with individual body segments. This combination of global and local features allows the framework to achieve a more accurate HMR, capturing subtle differences in human movements. Experiments demonstrate that our method with only sparse event data outperforms baseline methods.

In this paper, we propose a framework for 3D human pose estimation with a single 360° camera mounted on the user’s wrist. Perceiving a 3D human pose with such a simple setting has remarkable potential for various applications (e.g., daily-living activity monitoring, motion analysis for sports enhancement). However, no existing work has tackled this task due to the difficulty of estimating a human pose from a single camera image in which only a part of the human body is captured and the lack of training data. Therefore, we propose an effective method for translating wrist-mounted 360° camera images into 3D human poses. We also propose silhouette-based synthetic data generation dedicated to this task, which enables us to bridge the domain gap between real-world data and synthetic data. We achieved higher estimation accuracy quantitatively and qualitatively compared with other baseline methods.

We are developing a conceptual design for a power distribution line maintenance robot system using a CG (computer graphics) simulator and an experimental robot system. This system is a semi-autonomous robot system that performs tasks using task-level instructions. An operator directs the tasks, for example inserting a bolt into a hole, and clicks the work objects ldquoBoltrdquo and ldquoHolerdquo in the picture on the display with a mouse.