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Face animation aims to render the source image according to the motion of the driving images while preserving the identity information of the source image. Despite significant advancements with the introduction of additional information, the literature lacks a thorough exploration of identity preservation. This paper proposes a Prior Structure-Assisted Identity-Preserving (PSAIP) network for face animation. Specifically, we introduce the additional priors of portrait segmentation images and face landmarks, which provide rich face geometric information while masking background interference to generate more realistic face images. Furthermore, we design an identity-enhancing generator network to adaptively fuses the identity feature information extracted from the source portrait and motion representations. Also, we utilize an identity-aware feature loss based on segmentation to avoid background distractions and monitor the identity preservation ability of the model. Extensive experiments on the VoxCeleb dataset and HDTF dataset show that our method is superior in identity preservation and realistic visual effects. [ABSTRACT FROM AUTHOR]

The main goal of six-dimensional pose estimation is to accurately ascertain the location and orientation of an object in three-dimensional space, which has a wide range of applications in the field of artificial intelligence. Due to the relative sparseness of the point cloud data captured by the depth camera, the ability of models to fully understand the shape, structure, and other features of the object is hindered. Consequently, the model exhibits weak generalization when faced with objects with significant shape differences in the new scene. The deep integration of feature levels and the mining of local and global information can effectively alleviate the influence of the above factors. To solve these problems, we propose a new Two-Stage Geometric Neighborhood Fusion Network for category-level 6D pose estimation (TGNF-Net) to estimate objects that have not appeared in the training phase, which strengthens the fusion capacity of feature points within a specific range of neighborhoods, enabling the feature points to be more sensitive to both local and global geometric information. Our approach includes a neighborhood information fusion module, which can effectively utilize neighborhood information to enrich the feature set of different modal data and overcome the problem of heterogeneity between image and point cloud data. In addition to this, we design a two-stage geometric information embedding module, which can effectively fuse geometric information of the multi-scale range into keypoint features. This way enhances the robustness of the model and enables the model to exhibit stronger generalization capabilities when faced with unknown or complex scenes. These two strategies enhance the expression of features and make NOCS coordinate predictions more accurate. Many experiments show that our approach is superior to other classical methods on the CAMERA25, REAL275, HouseCat6D, and Omni6DPose datasets. [ABSTRACT FROM AUTHOR]

Background Colonoscopy is essential for diagnosing colorectal diseases but can cause pain during the procedure. This study explored the analgesic effects of transcutaneous electrical nerve stimulation based on wrist-ankle acupuncture theory (TENS-WAA) in colonoscopy without anesthesia. Methods This prospective study included 120 participants undergoing colonoscopies without anesthesia. The trial group received low-frequency, high-intensity TENS-WAA adjusted to the maximum tolerable current, while the control group received minimal current. The primary outcome was the retrospective pain score on a visual analog scale (VAS). Secondary outcomes included time, heart rate, and credibility/expectancy questionnaire (CEQ) scores. Results Participants who received TENS-WAA reported significantly lower pain VAS scores than the control group (estimated median difference –1.1, 95%CI –2 to –0.4; P = 0.002). Male participants in the trial group experienced significantly lower pain scores than the control group (mean difference –1.4, 95%CI –2.41 to –0.39; P = 0.008). The trial group showed significantly less variation in heart rates (P<0.001) and higher CEQ scores (P = 0.001) than the control group. No adverse events were reported. Conclusion TENS-WAA effectively reduced pain during colonoscopy without anesthesia, especially in male participants, providing a promising noninvasive analgesic method. [ABSTRACT FROM AUTHOR]

Image editing technology has brought about revolutionary changes in the field of architectural design, garnering significant attention in both the computer and architectural industries. However, architectural image editing is a challenging task due to the complex hierarchical structure of architectural images, which complicates the learning process for the high-dimensional features of architectural images. Some methods invert the images into the latent space of a pre-trained generative adversarial network (GAN) model, completing the editing process by manipulating this latent space. However, the task of striking a balance between reconstruction fidelity and editing efficacy through latent space mapping presents a formidable challenge. To address this issue, we propose a Residual Spatial Cross-Attention Network (RSCAN) for architectural image editing, which is an encoder model integrating multiple latent spaces. Specifically, we introduce the spatial feature extractor, which maps the image to the high-dimensional space F of the synthesis network, to enhance the spatial information retention and preserve the structural consistency of the architectural image. In addition, we propose the residual cross-attention to learn the mapping relationship between the low-dimensional space W and F space, generating modified features corresponding to the latent code and leveraging the benefits of multiple latent spaces to facilitate editing. Extensive experiments are performed on the LSUN Church dataset, and the experimental results indicate that our proposed RSCAN achieves significant improvements over the relevant methods in quantitative analysis metrics including the reconstruction quality, SSIM, FID, L2, LPIPS, PSNR, and editing effect Δ S , with enhancements of 29.49%, 17.29%, 8.81%, 11.43%, 11.26%, and 47.8%, respectively, thereby enhancing the practicality of architectural image editing. [ABSTRACT FROM AUTHOR]