MOVIN: Real-time Motion Capture using a Single LiDAR

Recent advancements in technology have brought forth new forms of interactive applications, such as the social metaverse, where end users interact with each other through their virtual avatars. In such applications, precise full-body tracking is essential for an immersive experience and a sense of embodiment with the virtual avatar. However, current motion capture systems are not easily accessible to end users due to their high cost, the requirement for special skills to operate them, or the discomfort associated with wearable devices. In this paper, we present MOVIN, the data-driven generative method for real-time motion capture with global tracking, using a single LiDAR sensor. Our autoregressive conditional variational autoencoder (CVAE) model learns the distribution of pose variations conditioned on the given 3D point cloud from LiDAR.As a central factor for high-accuracy motion capture, we propose a novel feature encoder to learn the correlation between the historical 3D point cloud data and global, local pose features, resulting in effective learning of the pose prior. Global pose features include root translation, rotation, and foot contacts, while local features comprise joint positions and rotations. Subsequently, a pose generator takes into account the sampled latent variable along with the features from the previous frame to generate a plausible current pose. Our framework accurately predicts the performer's 3D global information and local joint details while effectively considering temporally coherent movements across frames. We demonstrate the effectiveness of our architecture through quantitative and qualitative evaluations, comparing it against state-of-the-art methods. Additionally, we implement a real-time application to showcase our method in real-world scenarios. MOVIN dataset is available at \url{https://movin3d.github.io/movin_pg2023/}

LongT5: Efficient Text-To-Text Transformer for Long Sequences

Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models. In this paper, we present a new model, called LongT5, with which we explore the effects of scaling both the input length and model size at the same time. Specifically, we integrated attention ideas from long-input transformers (ETC), and adopted pre-training strategies from summarization pre-training (PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call {\em Transient Global} (TGlobal), which mimics ETC's local/global attention mechanism, but without requiring additional side-inputs. We are able to achieve state-of-the-art results on several summarization tasks and outperform the original T5 models on question answering tasks.Comment: Accepted in NAACL 202

Virus-templated Au and Au–Pt core–shell nanowires and their electrocatalytic activities for fuel cell applications

A facile synthetic route was developed to make Au nanowires (NWs) from surfactant-mediated bio-mineralization of a genetically engineered M13 phage with specific Au binding peptides. From the selective interaction between Au binding M13 phage and Au ions in aqueous solution, Au NWs with uniform diameter were synthesized at room temperature with yields greater than 98% without the need for size selection. The diameters of Au NWs were controlled from 10 nm to 50 nm. The Au NWs were found to be active for electrocatalytic oxidation of CO molecules for all sizes, where the activity was highly dependent on the surface facets of Au NWs. This low-temperature high yield method of preparing Au NWs was further extended to the synthesis of Au–Pt core–shell NWs with controlled coverage of Pt shell layers. Electro-catalytic studies of ethanol oxidation with different Pt loading showed enhanced activity relative to a commercial supported Pt catalyst, indicative of the dual functionality of Pt for the ethanol oxidation and Au for the anti-poisoning component of Pt. These new one-dimensional noble metal NWs with controlled compositions could facilitate the design of new alloy materials with tunable properties.United States. Army Research Office (Institute for Collaborative Biotechnologies, grant W911NF-09-0001)National Science Foundation (U.S.) (MRSEC Program, award no. DMR–0819762)Samsung (Firm) (Samsung Foundation of Culture, Samsung Scholarship