论文标题
无监督的学习有效的几何感知神经表达式
Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations
论文作者
论文摘要
我们提出了一种无监督的方法，用于对铰接对象的3D几何形式表示学习，其中没有图像置孔对或前景口罩用于训练。尽管可以通过现有的3D神经表示的明确姿势控制铰接物体的影像图像可以呈现，但是这些方法需要地面真相3D姿势和前景口罩进行训练，这是昂贵的。我们通过通过GAN培训来学习表示形式来消除这种需求。对发电机进行了训练，可以通过对抗训练从随机姿势和潜在向量产生逼真的铰接物体图像。为了避免用于GAN培训的高计算成本，我们提出了基于三个平面图的铰接对象的有效神经表示，然后为其无监督培训提供了基于GAN的框架。实验证明了我们方法的效率，并表明基于GAN的培训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.
