Hair & garment synthesis using deep learning method

For both AR and VR applications, there is a strong motivation to generate virtual avatars with realistic hairs and garments that are the two most significant elements to personify any character. However, due to the complex structures and ever-changing fashion styles, modeling hairs and garments still remain tedious and expensive as they require considerable professional effort. My research interest focuses on deep learning methods in 3D modeling, rendering, and animation, especially to synthesis high-quality hairs and garments with plausible details. In this talk, I will present the progression of three projects: (i) Hair-GAN: recovering 3D hair structure from a single image using the generative adversarial network; (ii) deep detail enhancement for any garment; (iii) dynamic neural garments. The first project introduces an architecture of a generative adversarial network to recover 3D hair structure from a single image. The second presents a method to synthesize plausible wrinkle details on a coarse garment geometry. The third project proposes a new garment system that jointly simulates and synthesizes the dynamic appearance of a target garment directly starting from a 3D body motion sequence.