Video anomaly detection is challenging because abnormal events are unbounded, rare, equivocal, irregular in real scenes. In recent years, transformers have demonstrated powerful modelling abilities for sequence data. Thus, we attempt to apply transformers to video anomaly detection. In this paper, we propose a prediction-based video anomaly detection approach named TransAnomaly. Our model combines the U-Net and the Video Vision Transformer (ViViT) to capture richer temporal information and more global contexts. To make full use of the ViViT for the prediction, we modified the ViViT to make it capable of video prediction. Experiments on benchmark datasets show that the addition of the transformer module improves the anomaly detection performance. In addition, we calculate regularity scores with sliding windows and evaluate the impact of different window sizes and strides. With proper settings, our model outperforms other state-of-the-art prediction-based video anomaly detection approaches. Furthermore, our model can perform anomaly localization by tracking the location of patches with lower regularity scores.
We present a novel approach that constructs 3D virtual garment models from photos. Unlike previous methods that require photos of a garment on a human model or a mannequin, our approach can work with various states of the garment: on a model, on a mannequin, or on a flat surface. To construct a complete 3D virtual model, our approach only requires two images as input, one front view and one back view. We first apply a multi-task learning network called JFNet that jointly predicts fashion landmarks and parses a garment image into semantic parts. The predicted landmarks are used for estimating sizing information of the garment. Then, a template garment mesh is deformed based on the sizing information to generate the final 3D model. The semantic parts are utilized for extracting color textures from input images. The results of our approach can be used in various Virtual Reality and Mixed Reality applications.
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