3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3D bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurements, where the realistic costs for annotating a frame and a 3D bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.

Text design is one of the most critical procedures in poster design, as it relies heavily on the creativity and expertise of humans to design text images considering the visual harmony and text-semantic. This study introduces TextPainter, a novel multimodal approach that leverages contextual visual information and corresponding text semantics to generate text images. Specifically, TextPainter takes the global-local background image as a hint of style and guides the text image generation with visual harmony. Furthermore, we leverage the language model and introduce a text comprehension module to achieve both sentence-level and word-level style variations. Besides, we construct the PosterT80K dataset, consisting of about 80K posters annotated with sentence-level bounding boxes and text contents. We hope this dataset will pave the way for further research on multimodal text image generation. Extensive quantitative and qualitative experiments demonstrate that TextPainter can generate visually-and-semantically-harmonious text images for posters.

Advertising posters, a form of information presentation, combine visual and linguistic modalities. Creating a poster involves multiple steps and necessitates design experience and creativity. This paper introduces AutoPoster, a highly automatic and content-aware system for generating advertising posters. With only product images and titles as inputs, AutoPoster can automatically produce posters of varying sizes through four key stages: image cleaning and retargeting, layout generation, tagline generation, and style attribute prediction. To ensure visual harmony of posters, two content-aware models are incorporated for layout and tagline generation. Moreover, we propose a novel multi-task Style Attribute Predictor (SAP) to jointly predict visual style attributes. Meanwhile, to our knowledge, we propose the first poster generation dataset that includes visual attribute annotations for over 76k posters. Qualitative and quantitative outcomes from user studies and experiments substantiate the efficacy of our system and the aesthetic superiority of the generated posters compared to other poster generation methods.

The facial caricature shows the distinct characteristics of a person via exaggerations of both shape and appearance. This paper presents a novel framework that automatically generates vivid facial caricatures by encoding personalized semantic information. To this end, we first design a part-based scheme for geometry warping, which composes local semantic deformation into a global warping field, equipped with sufficient warping freedom of different facial components. Second, under the scheme of Part-based Warping, we design a photo-to-caricature translation network called PbWarpGAN, and adopt several novel losses to capture the personalized characteristics of each input face and preserve its identity better. Third, based on PbWarpGAN, we develop a user-friendly interface by introducing an attention scheme on each facial component, allowing ordinary users to adjust the automatically generated caricature by PbWarpGAN according to their preference conveniently. Experimental results show that our PbWarpGAN is more effective in capturing personalized characteristics than counterparts, and provides an efficient tool for caricature designing application.

Video conferencing is an essential way for contactless conversation, which conveys abundant multimedia signals. Especially under COVID-19, the video conference has been becoming a common way for daily communications. However, for the sake of plague prevention, it usually happens that the people attending the video conference are wearing a mouth mask, leading to inconvenient communication due to incomplete facial information. To tackle this problem, we develop a novel system that reveals the masked faces in real-time, making each participant feel like the others are mask-free. Moreover, we map the audio to 3DMM expression to guide the generation of various mouth shapes utilizing multi-modal information. Extensive experiments validate the revealing effectiveness and better user experience of the system. Furthermore, by applying lightweight networks design, the proposed system can run in real-time.

Face parsing computes pixel-wise label maps for different semantic components (e.g., hair, mouth, eyes) from face images. Existing face parsing literature have illustrated significant advantages by focusing on individual regions of interest (RoIs) for faces and facial components. However,the traditional crop-and-resize focusing mechanism ignores all contextual area outside the RoIs, and thus is not suitable when the component area is unpredictable, e.g. hair. Inspired by the physiological vision system of human, we propose a novel RoI Tanh-warping operator that combines the central vision and the peripheral vision together. It addresses the dilemma between a limited sized RoI for focusing and an unpredictable area of surrounding context for peripheral information. To this end, we propose a novel hybrid convolutional neural network for face parsing. It uses hierarchical local based method for inner facial components and global methods for outer facial components. The whole framework is simple and principled, and can be trained end-to-end. To facilitate future research of face parsing, we also manually relabel the training data of the HELEN dataset and will make it public. Experiments on both HELEN and LFW-PL benchmarks demonstrate that our method surpasses state-of-the-art methods.