Intelligent wearable systems have been widely used in health monitoring, motion tracking, and engineering safety. However, the single function of current wearable systems cannot satisfy the requirements of complex scenarios, and the wearable systems cannot establish a relationship with the virtual 3-D visualization platform. To address these issues, this article proposes a novel intelligent wearable system with motion and emotion recognition. Multiple sensors are integrated into the system to collect motion and emotion information. In order to achieve accurate classification and recognition of multiple sensor information, we propose a novel human action recognition network called the three-branch spatial–temporal feature extraction network (TB-SFENet), which can obtain more robust features and achieve an accuracy of 97.04% on the UCI-HAR data set and 92.68% on the UniMiB SHAR data set. To establish the relationship between the real entity and virtual space, we use digital twin (DT) technology to establish the 3-D display DT platform. The platform enables real-time information interaction, such as activity, emotion, location, and monitoring information. Additionally, we establish the TGAM electroencephalogram emotion classification (TEEC) data set, which contains 120 000 pieces of data, for the proposed system. Experimental results indicate that the proposed system realizes virtual reality (VR) information interaction between the personal digital human and actual person based on the intelligent wearable system, which has great potential for applications in intelligent healthcare, VR, and other fields.

An image-based virtual try-on system transfers an in-shop garment to the corresponding garment region of a reference person, which has huge application potential and commercial value in online clothing shopping. Existing methods have difficulty preserving garment texture and body details because of rough garment alignment and imperfect detail-retention strategies. To address this problem, we propose a virtual try-on network based on semantic constraints and flow alignment. The key idea of the framework is as follows: 1) a global-local semantic predictor (GLSP) is proposed to generate a reasonable target semantic map, which clearly guides the correct alignment of the in-shop garment with the body and the generation of try-on result; and 2) a novel appearance flow-based garment alignment network (AFGAN) is proposed to align the in-shop garment with the body, which is important to preserve maximum garment detail and ensure natural and realistic warping; and 3) we propose a synthesis strategy to integrate the aligned garment and the human body to preserve maximum body detail for generating a realistic result and preventing cross-occlusion and pixel confusion between different body parts. Experiments on the existing benchmark dataset demonstrate that the proposed method achieves the best performance on qualitative and quantitative experiments among the state-of-the-art virtual try-on techniques.

Multi-pose virtual try-on has become a research focus for online clothes shopping due to the fixed-pose virtual try-on methods that cannot provide a different pose try-on effect. The challenge of multi-pose virtual try-on is that the detailed information of a generated image is difficult to obtain in the pose transformation and garment distortion. To solve the issue, we propose a multi-pose virtual try-on method via appearance flow and feature filtering (VTON-MP). First, a segmentation generation network of 2D keypoints about the target pose is used to predict the body semantic distribution of the target pose. Second, the desired garment is distorted to correspond to the body posture using the appearance flow figure alignment network (AFFAN). Third, latent useless feature weights are restrained using a filtering-enhancement block (FEB), and effective appearance feature weights are enhanced. Finally, the spatial relationship of body parts in the resulting image is further optimized using spatially-adaptive instance normalization (SAIN). Compared to state-of-the-art methods of subjective and objective experiments on the MPV dataset, the proposed VTONMP achieves the best performance in terms of SSIM, PSNR and FID. The experimental results demonstrate that the proposed algorithm can better retain image details (head, hands, arms, and trousers).

Clothing parsing is a challenging task. In this task, there are two main inherent challenges, which are the fine-grained parsing of small clothing components and the missegmentation of similar clothing categories. To address these challenges, this paper proposes a phase contour enhancement network for clothing parsing. It achieves improved accuracy while maintaining a relatively small network complexity. Leveraging the distinctive features of phase maps, we introduce the phase contour enhancement attention (PCEA) module to augment the encoder’s object edge information. To further enrich the model’s feature extraction capacity, we present the dilated convolution pyramid (DCP) module, combining it with a lightweight decoder to achieve improved global context modeling. The synergistic integration of the PCEA and DCP modules enhances the network with remarkable capabilities in capturing intricate contours, effectively surmounting the challenges of fine-grained parsing for small clothing components and mitigating the missegmentation of similar clothing categories. Through extensive experiments on the CFP, Modanet, and DeepFashion2 datasets and further expansion experiments on the LIP dataset, our method demonstrates outstanding performance across multiple evaluation metrics, surpassing state-of-the-art methods for clothing parsing. In conclusion, our phase contour enhancement network exhibits remarkable performance in both clothing parsing tasks and extended human parsing tasks.

Clothing image classification is more and more important in the development of online clothing shopping. The clothing category marking, clothing commodity retrieval, and similar clothing recommendations are the popular applications in current clothing shopping, which are based on the technology of accurate clothing image classification. Wide varieties and various styles of clothing lead to great difficulty for the accurate clothing image classification. The traditional neural network can not obtain the spatial structure information of clothing images, which leads to poor classification accuracy. In order to reach the high accuracy, the enhanced capsule (EnCaps) network is proposed with the image feature and spatial structure feature. First, the spatial structure extraction model is proposed to obtain the clothing structure feature based on the EnCaps network. Second, the enhanced feature extraction model is proposed to extract more robust clothing features based on deeper network structure and attention mechanism. Third, parameter optimization is used to reduce the computation in the proposed network based on inception mechanism. Experimental results indicate that the proposed EnCaps network achieves high performance in terms of classification accuracy and computational efficiency.

Image-based virtual try-on systems have significant commercial value in online garment shopping. However, prior methods fail to appropriately handle details, so are defective in maintaining the original appearance of organizational items including arms, the neck, and in-shop garments. We propose a novel high fidelity virtual try-on network to generate realistic results. Specifically, a distributed pipeline is used for simultaneous generation of organizational items. First, the in-shop garment is warped using thin plate splines (TPS) to give a coarse shape reference, and then a corresponding target semantic map is generated, which can adaptively respond to the distribution of different items triggered by different garments. Second, organizational items are componentized separately using our novel semantic map-based image adjustment network (SMIAN) to avoid interference between body parts. Finally, all components are integrated to generate the overall result by SMIAN. A priori dual-modal information is incorporated in the tail layers of SMIAN to improve the convergence rate of the network. Experiments demonstrate that the proposed method can retain better details of condition information than current methods. Our method achieves convincing quantitative and qualitative results on existing benchmark datasets.