Real-time 3D hand mesh reconstruction is challenging, especially when the hand is holding some object. Beyond the previous methods, we design H2ONet to fully exploit non-occluded information from multiple frames to boost the reconstruction quality. First, we decouple hand mesh reconstruction into two branches, one to exploit finger-level nonoccluded information and the other to exploit global hand orientation, with lightweight structures to promote real-time inference. Second, we propose finger-level occlusionaware feature fusion, leveraging predicted finger-level occlusion information as guidance to fuse finger-level information across time frames. Further, we design hand-level occlusion-aware feature fusion to fetch non-occluded information from nearby time frames. We conduct experiments on the Dex-YCB and HO3D-v2 datasets with challenging hand-object occlusion cases, manifesting that H2ONet is able to run in real-time and achieves state-of-the-art performance on both the hand mesh and pose precision. The code will be released on GitHub.

3D hand-mesh reconstruction from RGB images facilitates many applications, including augmented reality (AR). However, this requires not only real-time speed and accurate hand pose and shape but also plausible mesh-image alignment. While existing works already achieve promising results, meeting all three requirements is very challenging. This paper presents a novel pipeline by decoupling the hand-mesh reconstruction task into three stages: a joint stage to predict hand joints and segmentation; a mesh stage to predict a rough hand mesh; and a refine stage to fine-tune it with an offset mesh for mesh-image alignment. With careful design in the network structure and in the loss functions, we can promote high-quality finger-level mesh-image alignment and drive the models together to deliver real-time predictions. Extensive quantitative and qualitative results on benchmark datasets demonstrate that the quality of our results outperforms the state-of-the-art methods on hand-mesh/pose precision and hand-image alignment. In the end, we also showcase several real-time AR scenarios.

Existing augmented reality (AR) applications often ignore the occlusion between real hands and virtual objects when incorporating virtual objects in user's views. The challenges come from the lack of accurate depth and mismatch between real and virtual depth. This paper presents GrabAR1, a new approach that directly predicts the real-and-virtual occlusion and bypasses the depth acquisition and inference. Our goal is to enhance AR applications with interactions between hand (real) and grabbable objects (virtual). With paired images of hand and object as inputs, we formulate a compact deep neural network that learns to generate the occlusion mask. To train the network, we compile a large dataset, including synthetic data and real data. We then embed the trained network in a prototyping AR system to support real-time grabbing of virtual objects. Further, we demonstrate the performance of our method on various virtual objects, compare our method with others through two user studies, and showcase a rich variety of interaction scenarios, in which we can use bare hand to grab virtual objects and directly manipulate them.

Wind-up toys are mechanical assemblies that perform intriguing motions driven by a simple spring motor. Due to the limited motor force and small body size, wind-up toys often employ higher pair joints of less frictional contacts and connector parts of nontrivial shapes to transfer motions. These unique characteristics make them hard to design and fabricate as compared to other automata. This paper presents a computational system to aid the design of wind-up toys, focusing on constructing a compact internal wind-up mechanism to realize user-requested part motions. Our key contributions include an analytical modeling of a wide variety of elemental mechanisms found in common wind-up toys, including their geometry and kinematics, conceptual design of wind-up mechanisms by computing motion transfer trees to realize the requested part motions, automatic construction of wind-up mechanisms by connecting multiple elemental mechanisms, and an optimization on the part and joint geometry with an objective of compacting the mechanism, reducing its weight, and avoiding collision. We use our system to design wind-up toys of various forms, fabricate a number of them using 3D printing, and show the functionality of various results.