DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling

Face modeling has been paid much attention in the field of visual computing. There exist many scenarios, including cartoon characters, avatars for social media, 3D face caricatures as well as face-related art and design, where low-cost interactive face modeling is a popular approach especially among amateur users. In this paper, we propose a deep learning based sketching system for 3D face and caricature modeling. This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features. A novel CNN based deep regression network is designed for inferring 3D face models from 2D sketches. Our network fuses both CNN and shape based features of the input sketch, and has two independent branches of fully connected layers generating independent subsets of coefficients for a bilinear face representation. Our system also supports gesture based interactions for users to further manipulate initial face models. Both user studies and numerical results indicate that our sketching system can help users create face models quickly and effectively. A significantly expanded face database with diverse identities, expressions and levels of exaggeration is constructed to promote further research and evaluation of face modeling techniques.Comment: 12 pages, 16 figures, to appear in SIGGRAPH 201

Additional treatment of wastewater reduces endocrine disruption in wild fish-A comparative study of tertiary and advanced treatments

The prediction of risks posed by pharmaceuticals and personal care products in the aquatic environment now and in the future is one of the top 20 research questions regarding these contaminants following growing concern for their biological effects on fish and other animals. To this end it is important that areas experiencing the greatest risk are identified, particularly in countries experiencing water stress, where dilution of pollutants entering river networks is more limited. This study is the first to use hydrological models to estimate concentrations of pharmaceutical and natural steroid estrogens in a water stressed catchment in South Australia alongside a UK catchment and to forecast their concentrations in 2050 based on demographic and climate change predictions. The results show that despite their differing climates and demographics, modeled concentrations of steroid estrogens in effluents from Australian sewage treatment works and a receiving river were similar to those observed in the UK and Europe, exceeding the combined estradiol equivalent’s predicted no effect concentration for feminization in wild fish. Furthermore, by 2050 a moderate increase in estrogenic contamination and the potential risk to wildlife was predicted with up to a two-fold rise in concentrations