CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 201

Applying Opponent Modeling for Automatic bidding in Online Repeated Auctions

Online auction scenarios, such as bidding searches on advertising platforms, often require bidders to participate repeatedly in auctions for the same or similar items. We design an algorithm for adaptive automatic bidding in repeated auctions in which the seller and other bidders also update their strategies. We apply and improve the opponent modeling algorithm to allow bidders to learn optimal bidding strategies in this multiagent reinforcement learning environment. The algorithm uses almost no private information about the opponent or restrictions on the strategy space, so it can be extended to multiple scenarios. Our algorithm improves the utility compared to both static bidding strategies and dynamic learning strategies. We hope the application of opponent modeling in auctions will promote the research of automatic bidding strategies in online auctions and the design of non-incentive compatible auction mechanisms

Polystyrene-b-poly(oligo(ethylene oxide) monomethyl ether methacrylate)-bpolystyrene triblock copolymers as potential carriers for hydrophobic drugs

A simple and effective method is introduced to synthesize a series of polystyrene-b-poly(oligo(ethylene oxide) monomethyl ether methacrylate)-b- polystyrene (PSt-b-POEOMA-b-PSt) triblock copolymers. The structures of PSt-b-POEOMA-b-PSt copolymers were characterized by Fourier-transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (1H NMR) spectroscopy. The molecular weight and molecular weight distribution of the copolymer were measured by gel permeation chromatography (GPC). Furthermore£ the self-assembling and drug-loaded behaviours of three different ratios of PSt-b-POEOMA-b-PSt were studied. These copolymers could readily self-assemble into micelles in aqueous solution. The vitamin E-loaded copolymer micelles were produced by the dialysis method. The micelle size and core-shell structure of the block copolymer micelles and the drug-loaded micelles were confirmed by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The thermal properties of the copolymer micelles before and after drug-loaded were investigated by different scanning calorimetry (DSC). The results show that the micelle size is slightly increased with increasing the content of hydrophobic segments and the micelles are still core-shell spherical structures after drug-loaded. Moreover, the glass transition temperature (Tg) of polystyrene is reduced after the drug loaded. The drug loading content (DLC) of the copolymer micelles is 70%-80% by ultraviolet (UV) photolithography analysis. These properties indicate the micelles self-assembled from PSt-b- POEOMA-b- PSt copolymers would have potential as carriers for the encapsulation of hydrophobic drugs

Lightweight Photometric Stereo for Facial Details Recovery

Recently, 3D face reconstruction from a single image has achieved great success with the help of deep learning and shape prior knowledge, but they often fail to produce accurate geometry details. On the other hand, photometric stereo methods can recover reliable geometry details, but require dense inputs and need to solve a complex optimization problem. In this paper, we present a lightweight strategy that only requires sparse inputs or even a single image to recover high-fidelity face shapes with images captured under near-field lights. To this end, we construct a dataset containing 84 different subjects with 29 expressions under 3 different lights. Data augmentation is applied to enrich the data in terms of diversity in identity, lighting, expression, etc. With this constructed dataset, we propose a novel neural network specially designed for photometric stereo based 3D face reconstruction. Extensive experiments and comparisons demonstrate that our method can generate high-quality reconstruction results with one to three facial images captured under near-field lights. Our full framework is available at https://github.com/Juyong/FacePSNet.Comment: Accepted to CVPR2020. The source code is available https://github.com/Juyong/FacePSNe