Due to privacy and cost issues, the lack of large-scale labeled datasets limits the advancement of person re-identification. Existing methods use generative adversarial networks or game engine rendering for data augmentation to improve re-identification performance. However, these approaches struggle to maintain realistic images. This paper introduces a novel approach called Identity Diffuser, which uses diffusion models to generate synthetic data for the same identity with different poses. Our proposed framework incorporates identity-specific embeddings and target poses into the diffusion process, enabling the generation of realistic and diverse images that consistently preserve identity features. Guided by pretrained re-identification net and target pose heatmap, the framework learns transformation trajectories through forward and backward denoising steps in the diffusion models. This approach effectively maintains key pedestrian attributes across various poses. Experimental results on the Market1501 and DukeMTMC datasets demonstrate a notable improvement in performance, with a 1.73%/0.80% mAp increase in Market1501/DukeMTMC datasets compared with current state-of-the-art method. When less real data is included, the increment can be 5.1%/1.5%, separately.

Human identification at a distance (HID) faces challenges due to the difficulty of acquiring traditional biometric modalities like face and fingerprints. Gait recognition offers a viable solution since it can be captured at a distance. To promote progress in gait recognition and provide a fair evaluation platform, the International Competition on Human Identification at a Distance (HID) has been organized annually since 2020. Since 2023, the competition has adopted the challenging SUSTech-Competition dataset, which includes significant variations in clothing, carried objects, and view angles. No training data is provided, requiring participants to train their models using external datasets. Each year, the competition applies a different random seed to generate distinct evaluation splits, reducing the risk of overfitting and ensuring fair evaluation of cross-domain generalization. Although the previous two competitions (HID 2023 and HID 2024) already utilized this dataset, HID 2025 aimed explicitly to explore whether algorithmic improvements could surpass the accuracy limits observed previously. Despite these heightened challenges, participants again demonstrated significant advancements, with the highest accuracy reaching 94.2%, setting a new benchmark for this dataset. We also analyze key technical trends and outline potential directions for future research on gait recognition.

Clothing-change person re-identification (CC-ReID) aims to match persons even if they change clothes. Thus, extracting clothing-independent features is the key challenge in CC-ReID. Recently, many researches have primarily focused on auxiliary information to realize discriminative feature learning including soft-biometrics features, such as body shape and gaits, and additional clothes labels. However, this method does not fully capture personal information hidden in RGB images. Owing to pretrained vision-language models like CLIP, textual information can describe a person in such a way that it includes all details to capturing personal characteristics, e.g., gender, age, hairstyle, glass, and clothes. Considering that certain personal textual information remains unchanged in CC-ReID, we propose a novel CC-ReID method called Multi-Masked Prompt Learning(MMPL), which takes full advantage of the ability of CLIP to extract clothing-independent features for CC-ReID. MMPL can extract clothing-independent textual and visual features via Prompt Mask and Visual Mask, respectively. This approach enables to discard clothing information from the trained model. The proposed MMPL outperforms other state-of-the-art approaches, improving the rank-1 accuracy to 1.5% and 1.0% on PRCC and LTCC datasets, respectively.

Augmented reality (AR) systems support complex tasks like assembly by overlaying task-related content onto the real world. In recent years, the effort of designing and developing assembly task support systems in AR decreased with the availability of high potential head-mounted displays and provision of integrated development environments. Nevertheless, problems still arise when companies craft an effective AR task support system, particularly in the difficulty of selecting appropriate techniques and information-presentation methods, and the requirements that vary with each use case. In this study, we formulated a corresponding guideline, developed a selection aid tool that incorporates filtering based on the categorization of subtasks and the degree of freedom of available tracking, and evaluated their effectiveness in two experiments. First, to confirm effects on system design, we asked 18 participants to perform the design action of the AR system with the guideline for two tasks (PC assembly and rope work). Consequently, to verify the quality of the designed AR systems from Experiment 1, we asked another set of 20 participants to perform the same tasks with those systems. The results confirm that using the guideline can considerably lower efforts creating media and alleviate the error for a specific process. We envision our guideline and tool to be accessible as an online web page, assisting AR assembly task support system designer/developers worldwide.

We proposes a spatial augmented reality system "Touch de YEBISU Navi" that stimulate customers' purchasing willingness. This system uses the knowledge that the purchase rate will rise when a customer pick up a product, and conducts guidance so that contents about the relevant information of a product will not advance unless a customer pick up a product. Furthermore, in order to lengthen the time a customer has in hand, this system can change contents according to the position of a product in hand. To evaluate the effectiveness of this system, a demonstration experiment was conducted at YEBISU Memorial Museum Shop on 25th and 26th February 2017. As a result, we report that the degree of interest in a product has improved and the effect of stimulating purchasing willingness had been shown.