Pose-guided human image generation aims to render a source image in a specific pose. Current methods predominantly employ 2D-based signals, which exhibit inherent information deficits, as pose conditions. This leads to difficulty in establishing precise source-target appearance-pose correspondence and further causing uncertainty in predicting self-occluded regions’ appearance. To address these issues, we propose a 3D Pose Conditional Diffusion model (3DPCD) that leverages a human parametric model to integrate comprehensive and adjustable 3D control into forward–backward diffusion steps. Specifically, we employ Fourier-transformed SMPL-X as the 3D pose representation to facilitate precise source-target correspondence by understanding the complete pose information. Building on this, we further propose a hierarchical appearance-pose alignment method, which aligns appearance with the complete pose information at both global and local levels. Moreover, motivated by the fact that human pose transformation is a progressive process in 3D space and our 3D pose representation is adjustable, we integrate progressively interpolated 3D control into a series of sampling steps. This effectively mitigates uncertainties in pixel transfer between poses. It should be noted that the proposed explicit pose-guided strategy also supports flexible adjustment of pose, shape, and viewpoint. Both quantitative and qualitative evaluations demonstrate that our 3DPCD outperforms state-of-the-art methods on the widely used DeepFashion InShop benchmark and our newly constructed PoseWeb-33 dataset, which features richer appearance variations and more diverse conditional poses.

The widespread adoption of cloud storage has raised considerable data privacy concerns for outsourced databases. In recent years, Structured Encryption (STE) has emerged as a promising solution to build encrypted databases that efficiently handle queries while preserving privacy through underlying structures called Encrypted Multi-Maps (EMMs). However, current STE-based schemes primarily focus on static settings, and their direct extensions to dynamic settings introduce significant challenges in client storage overhead and update efficiency with join condition. In this paper, we present an efficient dynamic encrypted database scheme supporting large-scale data. To address the challenges in dynamic settings, we first propose a novel dynamic EMM design with constant client storage that utilizes a global counter to reduce client storage overhead. We then introduce an algorithm for dynamically handling join queries based on tags generated from values of the join attribute, significantly reducing update overhead. We implement our scheme and conduct comparative analyses with existing dynamic STE schemes. The experimental results demonstrate that our scheme offers significant advantages in terms of client storage overhead and update performance.

This study introduces a novel approach for the estimation of seismic source parameters using a multi-task learning network that incorporates a Fourier Transformer architecture. The Fourier Transformer is designed to extract information from both the time and frequency domains, which reduces the time complexity by utilizing Fast Fourier Transform (FFT) in place of the traditional attention mechanism in the Transformer encoder. The network consists of a shared encoder for general feature extraction and four task-specific decoders for parameter estimation. The model is both lightweight and accurate, capable of simultaneously estimating magnitude, epicentral distance, p travel time, and depth based on a 30-second single-station waveform. The proposed approach was validated using the Stanford Earthquake dataset (STEAD) and compared with the state-of-the-art techniques. The results show standard deviations of 0.19 for magnitude, 3.77 km for epicentral distance, 0.46 s for p travel time, and 5.77 km for depth, with a lower error and a faster response compared to the existing prediction framework. Code is available at https://github.com/KG-TSI-Civil/MFTnet.