Deformation transfer survey

Roberts, Rodney G.; Anjos, Rafael Kuffner dos; Maejima, Akinobu; Anjyo, Ken

doi:10.1016/j.cag.2020.10.004

Cited by 10 publications

(1 citation statement)

References 25 publications

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“…Three-dimensional (3D) pose transfer has attracted increasing interest in the computer vision and graphics community for decades. This has enabled several applications such as generating new poses and animation sequences [1,2] for making 3D movies and games. This is challenging when the given shapes differ significantly in intrinsic attributes, such as identity.…”

Section: Introductionmentioning

confidence: 99%

Geometry-aware 3D pose transfer using transformer autoencoder

Liu,

Gai,

et al. 2024

Comp. Visual Media

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Abstract3D pose transfer over unorganized point clouds is a challenging generation task, which transfers a source’s pose to a target shape and keeps the target’s identity. Recent deep models have learned deformations and used the target’s identity as a style to modulate the combined features of two shapes or the aligned vertices of the source shape. However, all operations in these models are point-wise and independent and ignore the geometric information on the surface and structure of the input shapes. This disadvantage severely limits the generation and generalization capabilities. In this study, we propose a geometry-aware method based on a novel transformer autoencoder to solve this problem. An efficient self-attention mechanism, that is, cross-covariance attention, was utilized across our framework to perceive the correlations between points at different distances. Specifically, the transformer encoder extracts the target shape’s local geometry details for identity attributes and the source shape’s global geometry structure for pose information. Our transformer decoder efficiently learns deformations and recovers identity properties by fusing and decoding the extracted features in a geometry attentional manner, which does not require corresponding information or modulation steps. The experiments demonstrated that the geometry-aware method achieved state-of-the-art performance in a 3D pose transfer task. The implementation code and data are available at https://github.com/SEULSH/Geometry-Aware-3D-Pose-Transfer-Using-Transformer-Autoencoder.

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Section: Introductionmentioning

confidence: 99%