Andreas Geiger

Publications of Xu Chen

SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes
X. Chen, Y. Zheng, M. Black, O. Hilliges and A. Geiger
International Conference on Computer Vision (ICCV), 2021
Abstract: Neural implicit surface representations have emerged as a promising paradigm to capture 3D shapes in a continuous and resolution-independent manner. However, adapting them to articulated shapes is non-trivial. Existing approaches learn a backward warp field that maps deformed to canonical points. However, this is problematic since the backward warp field is pose dependent and thus requires large amounts of data to learn. To address this, we introduce SNARF, which combines the advantages of linear blend skinning (LBS) for polygonal meshes with those of neural implicit surfaces by learning a forward deformation field without direct supervision. This deformation field is defined in canonical, pose-independent space, allowing for generalization to unseen poses. Learning the deformation field from posed meshes alone is challenging since the correspondences of deformed points are defined implicitly and may not be unique under changes of topology. We propose a forward skinning model that finds all canonical correspondences of any deformed point using iterative root finding. We derive analytical gradients via implicit differentiation, enabling end-to-end training from 3D meshes with bone transformations. Compared to state-of-the-art neural implicit representations, our approach generalizes better to unseen poses while preserving accuracy. We demonstrate our method in challenging scenarios on (clothed) 3D humans in diverse and unseen poses.
Latex Bibtex Citation:
  author = {Xu Chen and Yufeng Zheng and Michael Black and Otmar Hilliges and Andreas Geiger},
  title = {SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year = {2021}
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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