Geometry-aware 3D pose transfer using transformer autoencoder

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Shanghuan Liu, Shaoyan Gai, Feipeng Da, Fazal Waris
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Abstract

3D 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.

Abstract Image

利用变换器自动编码器实现几何感知三维姿态转移
在无组织点云上进行三维姿态转移是一项具有挑战性的生成任务,它需要将源姿态转移到目标形状,并保持目标的特征。最近的深度模型学习了变形,并将目标的身份作为一种样式,以调节两个形状的组合特征或源形状的对齐顶点。然而,这些模型中的所有操作都是点对点、独立的,忽略了输入形状的表面和结构的几何信息。这一缺点严重限制了生成和泛化能力。在本研究中,我们提出了一种基于新型变换器自动编码器的几何感知方法来解决这一问题。我们的框架采用了一种高效的自我关注机制,即交叉协方差关注,来感知不同距离点之间的相关性。具体来说,变换器编码器可提取目标形状的局部几何细节以获得身份属性,并提取源形状的全局几何结构以获得姿态信息。我们的变换器解码器以几何注意的方式对提取的特征进行融合和解码,从而有效地学习变形并恢复身份属性,这不需要相应的信息或调制步骤。实验证明,几何感知方法在三维姿态转移任务中取得了最先进的性能。实现代码和数据可在 https://github.com/SEULSH/Geometry-Aware-3D-Pose-Transfer-Using-Transformer-Autoencoder 上获取。
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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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