Andreas Geiger

Publications of Zijian Dong

PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence
Z. Dong, C. Guo, J. Song, X. Chen, A. Geiger and O. Hilliges
Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Abstract: We present a novel method to learn Personalized Implicit Neural Avatars (PINA) from a short RGB-D sequence. This allows non-expert users to create a detailed and personalized virtual copy of themselves, which can be animated with realistic clothing deformations. PINA does not require complete scans, nor does it require a prior learned from large datasets of clothed humans. Learning a complete avatar in this setting is challenging, since only few depth observations are available, which are noisy and incomplete (i.e.only partial visibility of the body per frame). We propose a method to learn the shape and non-rigid deformations via a pose-conditioned implicit surface and a deformation field, defined in canonical space. This allows us to fuse all partial observations into a single consistent canonical representation. Fusion is formulated as a global optimization problem over the pose, shape and skinning parameters. The method can learn neural avatars from real noisy RGB-D sequences for a diverse set of people and clothing styles and these avatars can be animated given unseen motion sequences.
Latex Bibtex Citation:
  author = {Zijian Dong and Chen Guo and Jie Song and Xu Chen and Andreas Geiger and Otmar Hilliges},
  title = {PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022}
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis
X. Chen, Z. Dong, J. Song, A. Geiger and O. Hilliges
European Conference on Computer Vision (ECCV), 2020
Abstract: Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances. In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module that is capable of implicitly representing the appearance, shape and pose of entire object categories, thus rendering the need for explicit CAD models per object instance unnecessary. The image synthesis network is designed to efficiently span the pose configuration space so that model capacity can be used to capture the shape and local appearance (i.e., texture) variations jointly. At inference time the synthesized images are compared to the target via an appearance based loss and the error signal is backpropagated through the network to the input parameters. Keeping the network parameters fixed, this allows for iterative optimization of the object pose, shape and appearance in a joint manner and we experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone. When provided with depth measurements, to overcome scale ambiguities, the method can accurately recover the full 6DOF pose successfully.
Latex Bibtex Citation:
  author = {Xu Chen and Zijian Dong and Jie Song and Andreas Geiger and Otmar Hilliges},
  title = {Category Level Object Pose Estimation via Neural Analysis-by-Synthesis},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2020}

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