In this study, we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting conditions. It combines the advantages of an explicit mesh and multi-layer perceptron (MLP) network as a hybrid representation to simplify the capture settings used in recent studies. After obtaining an initial shape through multi-view silhouettes, we introduced surface-based local MLPs to encode the vertex displacement field (VDF) for reconstructing surface details. The design of local MLPs allowed representation of the VDF in a piecewise manner using two-layer MLP networks to support the optimization algorithm. Defining local MLPs on the surface instead of on the volume also reduced the search space. Such a hybrid representation enabled us to relax the ray–pixel correspondences that represent the light path constraint to our designed ray–cell correspondences, which significantly simplified the implementation of a single-image-based environment-matting algorithm. We evaluated our representation and reconstruction algorithm on several transparent objects based on ground truth models. The experimental results show that our method produces high-quality reconstructions that are superior to those of state-of-the-art methods using a simplified data-acquisition setup.

Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks.