2 research outputs found
Learning Embedding of 3D models with Quadric Loss
Sharp features such as edges and corners play an important role in the
perception of 3D models. In order to capture them better, we propose quadric
loss, a point-surface loss function, which minimizes the quadric error between
the reconstructed points and the input surface. Computation of Quadric loss is
easy, efficient since the quadric matrices can be computed apriori, and is
fully differentiable, making quadric loss suitable for training point and mesh
based architectures. Through extensive experiments we show the merits and
demerits of quadric loss. When combined with Chamfer loss, quadric loss
achieves better reconstruction results as compared to any one of them or other
point-surface loss functions.Comment: Accepted to BMVC 2019 for Oral Presentatio
Neural Puppet: Generative Layered Cartoon Characters
We propose a learning based method for generating new animations of a cartoon
character given a few example images. Our method is designed to learn from a
traditionally animated sequence, where each frame is drawn by an artist, and
thus the input images lack any common structure, correspondences, or labels. We
express pose changes as a deformation of a layered 2.5D template mesh, and
devise a novel architecture that learns to predict mesh deformations matching
the template to a target image. This enables us to extract a common
low-dimensional structure from a diverse set of character poses. We combine
recent advances in differentiable rendering as well as mesh-aware models to
successfully align common template even if only a few character images are
available during training. In addition to coarse poses, character appearance
also varies due to shading, out-of-plane motions, and artistic effects. We
capture these subtle changes by applying an image translation network to refine
the mesh rendering, providing an end-to-end model to generate new animations of
a character with high visual quality. We demonstrate that our generative model
can be used to synthesize in-between frames and to create data-driven
deformation. Our template fitting procedure outperforms state-of-the-art
generic techniques for detecting image correspondences