The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and crime. A highly effective family of forgery image detection methods based on diffusion reconstruction error has emerged, as images generated by diffusion are more easily reconstructed by any diffusion model. However, we find that existing methods only use reconstruction error from a single time step, failing to fully leverage the entire reconstruction process. To this end, we propose to comprehensively consider every single time step to form the Temporal Reconstruction Error (TRE) that offers a richer feature representation. Furthermore, we design temporal aggregation and spatial focusing modules from two dimensions respectively to more effectively extract discriminative information from the TRE feature. Finally, we validate the proposed method on two popular datasets, and experimental results demonstrate that the proposed approach achieves state-of-the-art performance.

VTON (Virtual Try-ON), as an innovative visual application in e-commerce scenarios with great commercial value, has been widely studied in recent years. Due to its better robustness and realistic effect, deformation-synthesize-based VTON has become the dominant approach in this field. Existing clothing deformation techniques optimize the mapping relations between the original clothing image and the ground truth (GT) image of the worn clothing. However, there are color differences between the original and GT clothing images caused by lighting, warping, and occlusion. The color differences may lead to misaligned clothing mapping by only minimizing the cost of pixel value difference. Another drawback is that taking the parsing prediction as GT will bring alignment remnant, rooting in the processing order of parsing and deformation. Aiming above two drawbacks, we put forward SAME-VTON (Self-Adaptive clothing Mapping basEd Virtual Try-ON) for achieving realistic virtual try-on results. The core of SAME-VTON is the self-adaptive clothing mapping technique, composed of two parts: a color-adaptive clothing mapping module and a parsing-adaptive prediction process. In the color-adaptive clothing mapping module, we map each pixel of the target clothing with a combination of multiple pixel values from the original clothing image, which considers both the position and color changes. Furthermore, different combination weights are learned to increase the diversity of color mapping. In the parsing-adaptive prediction process, the color-adaptive clothing mapping module is adopted to deform clothing first, then the human parsing result is predicted under the reference of the deformed clothing, which can avoid alignment remnant. Extensive experiments demonstrate that the proposed SAME-VTON with the self-adaptive clothing mapping technique can achieve optimal mapping in the case of large color differences and obtain superior results compared with existing deformation-synthesize-based VTON.

Small Baseline Subset InSAR (SBAS InSAR) utilizes a series of synthetic aperture radar (SAR) interferograms to generate a time series that can analyze the surface displacements of coherent points. Still, atmospheric errors in interferometric SAR (InSAR) measurements can seriously affect the reliability of the surface displacement time series. In this article, a new approach incorporating the Generic Atmospheric Correction Online Service for InSAR (GACOS) and principal component analysis (PCA) is proposed to reduce atmospheric errors in SBAS InSAR. Its application to Southern California, USA suggests that the incorporation of GACOS and PCA can efficiently reduce atmospheric effects on InSAR observations and hence improve the accuracy of InSAR-derived surface displacements. The overall standard deviations of the SAR interferograms were reduced from 4.97 to 2.02 rad after applying GACOS and PCA with the root mean square error (RMSE) reducing by 61.1% from 18 to 7 mm. In addition, comparisons between different PCA processing strategies suggest that the more principal components are removed, the smaller the standard deviations of the interferograms, but the lower the accuracy of InSAR-derived surface displacements.

The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales. However, the expensive proprietary model images challenge the existing image virtual try-on methods in this scenario, as most of them need to be trained on considerable amounts of model images accompanied with paired clothes images. In this paper, we propose a cheap yet scalable weakly-supervised method called Deep Generative Projection (DGP) to address this specific scenario. Lying in the heart of the proposed method is to imitate the process of human predicting the wearing effect, which is an unsupervised imagination based on life experience rather than computation rules learned from supervisions. Here a pretrained StyleGAN is used to capture the practical experience of wearing. Experiments show that projecting the rough alignment of clothing and body onto the StyleGAN space can yield photo-realistic wearing results. Experiments on real scene proprietary model images demonstrate the superiority of DGP over several state-of-the-art supervised methods when generating clothing model images.

Image virtual try-on task has abundant applications and has become a hot research topic recently. Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothing, resulting in inaccurate dressing effect. In this paper, we put forward an akin task that aims to dress clothing for underwear models. To solve the above drawbacks, we propose a Shape Controllable Virtual Try-On Network (SC-VTON), where a graph attention network integrates the information of model and clothing to generate the warped clothing image. In addition, the control points are incorporated into SC-VTON for the desired clothing shape. Furthermore, by adding a Splitting Network and a Synthesis Network, we can use in-shop clothing/model pair data to help optimize the deformation module and generalize the task to the typical virtual try-on task. Extensive experiments show that the proposed method can achieve accurate shape control. Meanwhile, compared with other methods, our method can generate high-resolution results with detailed textures, which can be applied in real applications.