团队简介    研究论文    研究人员    发明专利    研究资助    联系我们    English
Automatic Unpaired Shape Deformation Transfer
Conditionally accepted by SIGGRAPH ASIA 2018 (ACM Transactions on Graphics).

Lin Gao, Jie Yang, Yi-Ling Qiao, Yu-Kun Lai, Paul Rosin, Weiwei Xu, Shihong Xia<

Abstract
Transferring deformation from a source shape to a target shape is a very useful technique in computer graphics. State-of-the-art deformation transfer methods require either point-wise correspondences between source and target shapes, or pairs of deformed source and target shapes with corresponding deformations. However, in most cases, such correspondences are not available and cannot be reliably established using an automatic algorithm. Therefore, substantial user efort is needed to label the correspondences or to obtain and specify such shape sets. In this work, we propose a novel approach to automatic deformation transfer between two unpaired shape sets without correspondences. 3D deformation is represented in a highdimensional space. To obtain a more compact and efective representation, two convolutional variational autoencoders are learned to encode source and target shapes to their latent spaces. We exploit a Generative Adversarial Network (GAN) to map deformed source shapes to deformed target shapes, both in the latent spaces, which ensures the obtained shapes from the mapping are indistinguishable from the target shapes. This is still an under-constrained problem, so we further utilize a reverse mapping from target shapes to source shapes and incorporate cycle consistency loss, i.e. applying both mappings should reverse to the input shape. This VAE-Cycle GAN (VC-GAN) architecture is used to build a reliable mapping between shape spaces. Finally, a similarity constraint is employed to ensure the mapping is consistent with visual similarity, achieved by learning a similarity neural network that takes the embedding vectors from the source and target latent spaces and predicts the light ield distance between the corresponding shapes. Experimental results show that our fully automatic method is able to obtain high-quality deformation transfer results with unpaired data sets, comparable or better than existing methods where strict correspondences are required.
Downloads

Automatic Unpaired Shape Deformation Transfer.  Conditionally accepted by SIGGRAPH ASIA 2018 (ACM Transactions on Graphics). [PDF 2,272KB]