Hand mesh reconstruction has attracted considerable attention in recent years, with various approaches and techniques being proposed. Some of these methods in-corporate complex components and designs, which, while effective, may complicate the model and hinder efficiency. In this paper, we decompose the mesh decoder into token generator and mesh regressor. Through extensive ablation experiments, we found that the token generator should select discriminating and representative points, while the mesh regressor needs to upsample sparse keypoints into dense meshes in multiple stages. Given these function-alities, we can achieve high performance with minimal computational resources. Based on this observation, we propose a simple yet effective baseline that outperforms state-of-the-art methods by a large margin, while maintaining real-time efficiency. Our method outperforms existing solutions, achieving state-of-the-art (SOTA) results across multiple datasets. On the FreiHAND dataset, our approach produced a PA-MPJPE of 5.8mm and a PA-MPVPE of 6.1mm. Similarly, on the DexYCB dataset, we observed a PA-MPJPE of 5.5mm and a PA-MPVPE of 5.5mm. As for performance speed, our method reached up to 33 frames per second (fps) when using HRNet and up to 70 fps when employing FastViT-MA36. Code will be made available.

Recently, deep convolutional neural networks (CNNs) have made remarkable progress on single image super-resolution (SISR). However, many of these methods use very deep or wide convolutional layers to achieve good performance, which treat all feature channels indiscriminately and neglect the difference among the contribution of each channel to the output results. In this paper, we propose a low complexity solution based on channel splitting and fusion network (CSFN) to address this problem. Our method uses channel splitting and channel fusion to enhance feature maps and make full use of valuable information, and then multiple residual channel splitting and fusion blocks (CSFB) are cascaded to continuously extract more important information for reconstruction. To further minimize redundant parameters and improve efficiency, we adopt group and recursive con-volutional layer strategy in CSFB. Experiments demonstrate that our proposed CSFN could achieve higher performance with low computational complexity than most state-of-the-art methods.

Recently, extensive works based on convolutional neural network (CNN) have shown great success in single image super-resolution (SISR). In order to improve the SISR performance while reducing the number of model parameters, some methods adopt multiple recursive layers to enhance the intermediate features. However, in the recursive process, these methods only use the output features of current stage as the input of the next stage and neglect the output features of historical stages, which degrades the performance of the recursive blocks. The long-term dependencies can only be learned implicitly during the recursive processes. To address these issues, we propose the memory recursive network (MRNet) to make full use of the output features at each stage. The proposed MRNet utilizes a memory recursive module (MRM) to generate features for each recursive stage, and then these features are fused by our proposed ShuffleConv block. Specifically, MRM adopts a memory updater block to explicitly model the long-term dependencies between the output features of historical recursive stages. The output features from the memory updater will be used as the input of the next recursive stage and will be continuously updated during the recursions. To reduce the number of parameters and ease the training difficulty, we introduce a ShuffleConv module to fuse the features from different recursive stages, which is much more effective than using plain convolutional combinations. Comprehensive experiments demonstrate that the proposed MRNet achieves state-of-the-art SISR performance while using much fewer parameters.

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies. However, OAM communication system requires perfect alignment of the transmit and the receive antennas and this harsh precondition greatly challenges practical application of OAM mode multiplexing gain. In this paper, we first investigate the effect of non- parallel misalignment on the inter-mode interference of the RF-OAM communication system equipped with uniform circular array (UCA). Then, large-scale system analysis shows that increasing the number of antennas has little effect on the inter-mode interference induced by oblique angle, but beam steering approach could eliminate the inter-mode interference completely. Numerical simulations validate our conclusion on the effect of beam steering.