Computer aided design (CAD) technology is widely used for architectural design, but current CAD tools still require high-level design specifications from human. It would be significant to construct an intelligent CAD system allowing automatic architectural layout parsing (AutoALP), which generates candidate designs or predicts architectural attributes without much user intervention. To tackle these problems, many learning-based methods were proposed, and benchmark dataset become one of the essential elements for the data-driven AutoALP. This paper proposes a new dataset called SCUT-AutoALP for multi-paradigm applications. It contains two subsets: 1) Subset-I is for floor plan design containing 300 residential floor plan images with layout, boundary and attribute labels; 2) Subset-II is for urban plan design containing 302 campus plan images with layout, boundary and attribute labels. We analyzed the samples and labels statistically, and evaluated SCUT-AutoALP for different layout parsing tasks of floor plan/urban plan based on conditional generative adversarial networks (cGAN) models. The results verify the effectiveness and indicate the potential applications of SCUT-AutoALP. The dataset is available at https://github.com/designfuturelab702/SCUT-AutoALP-Database-Release.

We propose a new face relighting method using an illuminance template generated from a single reference portrait. First, the reference is wrapped according to the shape of the target. Second, we employ a new spatially variant edge-preserving smoothing filter to remove the facial identity and texture details of the wrapped reference, and obtain the illumination template. Finally, we relight the target with the template in CIELAB color space. Experiments show the effectiveness of our method for both grayscale and color faces taken from different databases, and the comparisons with previous works demonstrate a better relighting effect produced by our method.

Recently, many AI-aided layout design systems are developed to reduce tedious manual intervention based on deep learning. However, most methods focus on a specific generation task. This paper explores a challenging problem to obtain multiple layout design generation (LDG), which generates floor plan or urban plan from a boundary input under a unified framework. One of the main challenges of multiple LDG is to obtain reasonable topological structures of layout generation with irregular boundaries and layout elements for different types of design. This paper formulates the multiple LDG task as an image-to-image translation problem, and proposes a conditional generative adversarial network (GAN), called LDGAN, with adaptive modules. The framework of LDGAN is based on a generator-discriminator architecture, where the generator is integrated with conditional convolution constrained by the boundary input and the attention module with channel and spatial features. Qualitative and quantitative experiments were conducted on the SCUT-AutoALP and RPLAN datasets, and the comparison with the state-of-the-art methods illustrate the effectiveness and superiority of the proposed LDGAN.

Video text detection (VTD) aims to localize text instances in videos, which has wide applications for downstream tasks. To deal with the variances of different scenes and text instances, multiple models and feature fusion strategies were typically integrated in existing VTD methods. A VTD method consisting of sophisticated components can efficiently improve detection accuracy, but may suffer from a limitation for real-time applications. This paper aims to achieve real-time VTD with an adaptive lightweight end-to-end framework. Different from previous methods that represent text in a spatial domain, we model text instances in the Fourier domain. Specifically, we propose a scale-aware Fourier Contour Embedding method, which not only models arbitrary shaped text contours of videos as compact signatures, but also adaptively select proper scales for features in a backbone in the training stage. Then, we construct VTD-FCENet to achieve real-time VTD, which encodes temporal correlations of adjacent frames with scale-aware FCE in a lightweight and adaptive manner. Quantitative evaluations were conducted on ICDAR2013 Video, Minetto and YVT benchmark datasets, and the results show that our VTD-FCENet not only obtains the state-of-the-arts or competitive detection accuracy, but also allows real-time text detection on HD videos simultaneously.