Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations-猫眼课题宝

DOI：

10.48550/arxiv.2204.08839

发表时间：

2022-04

期刊：

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其他

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作者：

Atsuhiro Noguchi;Xiao Sun;Stephen Lin;Tatsuya Harada

我们提出了一种无监督的方法，用于关节对象的3D几何感知表示学习，其中不使用图像姿势对或前景掩模进行训练。虽然可以通过现有的3D神经表示用显式姿态控制来渲染铰接对象的照片级真实感图像，但这些方法需要用于训练的地面真实3D姿态和前景掩模，这是昂贵的。我们通过GAN训练来学习表示来满足这一需求。该生成器通过对抗训练从随机姿态和潜在向量生成铰接对象的真实图像。为了避免GAN训练的高计算成本，我们提出了一种基于三平面的铰接对象的有效神经表示，然后提出了一种基于GAN的无监督训练框架。实验证明了我们方法的有效性，并表明基于GAN的训练可以在没有配对监督的情况下学习可控的3D表示。

We propose an unsupervised method for 3D geometry-aware representation learning of articulated objects, in which no image-pose pairs or foreground masks are used for training. Though photorealistic images of articulated objects can be rendered with explicit pose control through existing 3D neural representations, these methods require ground truth 3D pose and foreground masks for training, which are expensive to obtain. We obviate this need by learning the representations with GAN training. The generator is trained to produce realistic images of articulated objects from random poses and latent vectors by adversarial training. To avoid a high computational cost for GAN training, we propose an efficient neural representation for articulated objects based on tri-planes and then present a GAN-based framework for its unsupervised training. Experiments demonstrate the efficiency of our method and show that GAN-based training enables the learning of controllable 3D representations without paired supervision.

Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations

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