Restricted by the limited interaction area of native AR glasses, creating sketches is a challenge in it. Existing solutions attempt to use mobile devices (e.g., tablets) or mid-air hand gestures to expand the interactive spaces and as the 2D/3D sketching input interfaces for AR glasses. Between them, mobile devices allow for accurate sketching but are often heavy to carry. Sketching with bare hands is zero-burden but can be inaccurate due to arm instability. In addition, mid-air sketching can easily lead to social misunderstandings and its prolonged use can cause arm fatigue. In this work, we present WristSketcher, a new AR system based on a flexible sensing wristband that enables users to place multiple virtual plane canvases in the real environment and create 2D dynamic sketches based on them, featuring an almost zero-burden authoring model for accurate and comfortable sketch creation in real-world scenarios. Specifically, we streamlined the interaction space from the mid-air to the surface of a lightweight sensing wristband, and implemented AR sketching and associated interaction commands by developing a gesture recognition method based on the sensing pressure points. We designed a set of interactive gestures consisting of Long Press, Tap and Double Tap based on a heuristic study involving 26 participants. These gestures are correspondingly mapped to various command interactions using a combination of multi-touch and hotspots. Moreover, we endow our WristSketcher with the ability of animation creation, allowing it to create dynamic and expressive sketches. Experimental results demonstrate that our WristSketcher (i) recognizes users’ gesture interactions with a high accuracy of 95.9%; (ii) achieves higher sketching accuracy than Freehand sketching; (iii) achieves high user satisfaction in ease of use, usability and functionality; and (iv) shows innovation potentials in art creation, memory aids, and entertainment applications.

Estimating 3D human poses from monocular images is an important research area with many practical applications. However, the depth ambiguity of 2D solutions limits their accuracy in actions where occlusion exits or where slight centroid shifts can result in significant 3D pose variations. In this paper, we introduce a novel multimodal approach to mitigate the depth ambiguity inherent in monocular solutions by integrating spatial-aware pressure information. We first establish a data collection system with a pressure mat and a monocular camera, and construct a large-scale multimodal human activity dataset comprising over 600,000 frames of motion data. Utilizing this dataset, we propose a pressure image reconstruction network to extract pressure priors from monocular images. Subsequently, we introduce a Transformer-based multimodal pose estimation network to combine pressure priors with monocular images, achieving a world mean per joint position error of 51.6mm, outperforming state-of-the-art methods. Extensive experiments demonstrate the effectiveness of our multimodal 3D human pose estimation method across various actions and joints, highlighting the significance of spatial-aware pressure in improving the accuracy of monocular-vision-based methods. Our dataset is available at: https://github.com/LishuangZhan/SATPose.