The famous Barnes–Wall lattices can be obtained by applying Construction D to a chain of Reed–Muller codes. By applying Construction${\text {D}}^{\text {(cyc)}}$to a chain of extended cyclic codes sandwiched between Reed–Muller codes, Hu and Nebe (J. London Math. Soc.(2)101 (2020) 1068-1089) constructed new series of universally strongly perfect lattices sandwiched between Barnes–Wall lattices. In this paper, we first extend their construction to generalized Reed–Muller codes, and then explicitly determine the minimum vectors of those new sandwiched Reed–Muller codes for some special cases.

Transformer has become the dominant model in natural language processing (NLP). Researchers have recently attempted to exploit transformer architecture for various computer vision tasks and achieved competitive results. However, few works have been done to explore transformer architecture for 3D human pose estimation (HPE). In this work, we propose cascade transformers, a novel transformer-based method for 3D HPE from a single image. Specifically, our cascade transformers consist of two transformer encoders exploiting static and dynamic human joint relations respectively. Leveraging the self-attention module and the cascade structure, our method comprehensively models the static and dynamic human joint relations. We evaluate our method on Human3.6M. Extensive experiments show that our method achieves excellent performance without explicitly using human skeleton priors. Notably, our single-image method achieves approximately the same performance as the current best transformer-based method PoseFormer even when PoseFormer uses 9 frames to predict pose.

Speech-driven lip sync has become a promising technique for generating and editing talking-head videos. These studies mainly use 3D morphable models or 2D facial landmarks as the intermediate face representations. However, 2D-based methods have been stagnant recently due to their inability to handle out-of-plane rotations, even though the 2D landmarks have the advantage of fast and accurate extraction. In this paper, we design a cascaded temporal convolutional network to successively generate mouth shapes and corresponding jawlines based on audio signals and template headposes. Instead of explicitly calibrating the rotation between the predicted mouth and the template face, we employ neural networks to learn the pose-adaptive mapping implicitly. We also propose an image-to-image translation-based neural rendering method for producing high-resolution and photo-realistic videos. Experiments show our solution improves both the mapping accuracy and visual performance than baselines. This work could benefit many real-world applications like virtual anchors, telepresence, and conversational agents.

Lip sync has emerged as a promising technique for generating mouth movements from audio signals. However, synthesizing a high-resolution and photorealistic virtual news anchor is still challenging. Lack of natural appearance, visual consistency, and processing efficiency are the main problems with existing methods. In this paper, we present a novel lip-sync framework specially designed for producing high fidelity virtual news anchors. A pair of Temporal Convolutional Networks are used to learn the cross-modal sequential mapping from audio signals to mouth movements, followed by a neural rendering network that translates the synthetic facial map into high-resolution and photorealistic appearance. This fully-trainable framework provides an end-to-end processing that outperforms traditional graphics-based methods in many low-delay applications. Experiments also show the framework has advantages over modern neural-based methods in both visual appearance and efficiency.

With the rapid development of information technology, automation systems and virtual reality technology have become important pillars of economic and social development. As a key institution to store books and information materials, develop information resources and provide knowledge support, libraries are constructing and optimizing their systems. Based on the literature review about library automation systems and virtual reality application, this paper analyzes their expectable influence to promote more concern from scholars and technical personnel, and thereby, enhance the overall management and service level of libraries, especially for smart libraries, which represent the development trend of this smart+ era.