This paper tackles the emerging challenge of multi-view virtual try-on, utilizing both front- and back-view clothing images as inputs. Extending frontal try-on methods to a multi-view context is not straightforward. Simply concatenating the two input views or encoding their features for a generative model, such as a diffusion model, often fails to produce satisfactory results. The main challenge lies in effectively extracting and fusing meaningful clothing features from these input views. Existing explicit warping-based methods, which establish direct correspondence between input and target views, tend to introduce artifacts, particularly when there is a significant disparity between the input and target views. Conversely, implicit encoding-based methods often lose spatial information about clothing, resulting in outputs that lack detail. To overcome these challenges, we propose Robust-MVTON, an end-to-end method for robust and high-quality multi-view try-ons. Our approach introduces a novel cross-pose feature alignment technique to guide the fusion of clothing features and incorporates a newly designed loss function for training. With the fused multi-scale clothing features, we employ a coarse-to-fine diffusion model to generate realistic and detailed results. Extensive experiments conducted on the Deepfashion and MPV datasets affirm the superiority of our method, achieving state-of-the-art performance.

The image-based Person-to-Person (P2P) virtual try-on, involving the direct transfer of garments from one person to another, is one of the most promising applications of human-centric image generation. However, existing approaches struggle to accurately learn the clothing deformation when directly warping the garment from the source pose onto the target pose. To address this, we propose Person-to-Person virtual try-on via Garment UNwrapping, a novel framework dubbed as ViTon-GUN. Specifically, we divide the P2P task into two subtasks: Person-to-Garment (P2G) and Garment-to-Person (G2P). The P2G aims to unwrap the target garment from a source pose to a canonical representation based on A-Pose. In the P2G stage, we enable the implementation of a flow-based P2G scheme by introducing an A-Pose estimator and establishing comprehensive training conditions. Building upon this step-wise strategy, we introduce a novel pipeline for P2P try-on. Once trained, the P2G strategy can serve as a "plug-and-play" module, which efficiently adapts existing diffusion-based pre-trained G2P models to P2P try-on without further training. Quantitative and qualitative experiments demonstrate that our ViTon-GUN performs remarkably well on P2P try-on, even for dresses with intricate design details.

A dynamic random access memory (DRAM) relies on periodic refresh operations to prevent data loss caused by charge leakage. As memory capacities continue to grow, refresh power consumption accounts for an increasing proportion of the total DRAM power, and in some contexts, it even becomes a major contributor to power consumption. To address this issue, previous research has explored the tradeoff between DRAM reliability and refresh overhead. However, DRAM reliability degrades as technology nodes advance, making these approaches inapplicable in scenarios, such as servers, where high data reliability is critical. Furthermore, these approaches require modifications to the standard DRAM interface protocol and memory controller (MC), rendering them infeasible for standalone use in computer systems. In this article, we propose an energy-efficient charge-recycling DRAM (CR-DRAM), which enables multiple rounds of charge (i.e., energy) recycling between subarrays within a single autorefresh (AR) process. After refreshing a row, CR-DRAM reuses the charge stored in the bitline (BL) capacitors to supply power for refreshing the next row in another subarray, rather than discharging them directly. Since CR-DRAM is compatible with the joint electron device engineering council (JEDEC) interface standard, it can be easily integrated into modern computer systems. Our circuit-level simulation shows that CR-DRAM significantly reduces AR power consumption by 33.9% compared with conventional DRAM, with a modest area overhead of less than 0.9%. Furthermore, our system-level evaluation shows that CR-DRAM offers an average energy savings of 9.2% (maximum of 11.9%) compared with 8-Gb double data rate 4 (DDR4) DRAM across SPEC-2006 benchmark workloads.

Adaptive broadband digital beamforming (ABDBF) is an essential topic in the realm of array antenna for radar systems, because the array antenna with ABDBF could obtain wide swath and high azimuth resolution. Performance degradation at low snapshots is a serious problem for ABDBF. In this letter, the broadband beamforming weight generation network (BWGN) is proposed to quickly generate weights for ABDBF under low signal snapshot scenarios. The BWGN leverages the complex convolutional neural network to represent the mapping between input signals and output weights, which avoids the operation of covariance matrix and its inversion, thus speeding up the generation of weights. Compared with the existing neural network-based broadband beamforming method, wideband beamforming prediction network (WBPNet), training BWGN saves 71.45% of the time. Furthermore, progressive learning is introduced to the training process of BWGN, namely PL-BWGN, which further reduces training time of BWGN to 0.7529 h (h: hours). Simulation results demonstrate performance superiority of the proposed method compared with existing beamformers under low snapshot scenarios.

Robot-catching of in-flight objects is a challenging task, requiring a high-frequency sequence of pose estimation, trajectory prediction, catching point determination, and motion planning. Considering the small working space and visual occlusion in the natural environment, we investigate robot-catching with a short-distance and partially observable trajectory in this paper. We introduce a marker-based high-speed visual tracking method to collect sufficient data from the limited trajectory. Besides, we design a new catching point selection strategy to achieve a timely and stable response of the robot arm. Based on the proposed method and the dynamics of a thrown ring, we get a success rate of 90% in experiments to catch the in-flight rings using a collaborative robot arm.