论文标题
AE-NERF：3D感知物体操纵的自动编码神经辐射场
AE-NeRF: Auto-Encoding Neural Radiance Fields for 3D-Aware Object Manipulation
论文作者
论文摘要
我们为3D感知对象操作提出了一个新型框架，称为自动编码神经辐射场（AE-NERF）。我们的模型是在自动编码器体系结构中配制的，它提取了图像中脱离的3D属性，例如3D形状，外观和摄像头姿势，并通过dentanged生成神经辐射场（NERF）从属性中呈现出高质量的图像。为了提高分离能力，我们提出了两种损失，全球属性属性一致性损失在输入和输出之间定义，以及交换 - 分类分类损失。由于从头开始训练此类自动编码网络而没有地面形状和外观信息，因此我们提出了阶段训练方案，这极大地有助于提高性能。我们进行实验，以证明拟议模型对最新方法的有效性，并提供广泛的消融研究。
We propose a novel framework for 3D-aware object manipulation, called Auto-Encoding Neural Radiance Fields (AE-NeRF). Our model, which is formulated in an auto-encoder architecture, extracts disentangled 3D attributes such as 3D shape, appearance, and camera pose from an image, and a high-quality image is rendered from the attributes through disentangled generative Neural Radiance Fields (NeRF). To improve the disentanglement ability, we present two losses, global-local attribute consistency loss defined between input and output, and swapped-attribute classification loss. Since training such auto-encoding networks from scratch without ground-truth shape and appearance information is non-trivial, we present a stage-wise training scheme, which dramatically helps to boost the performance. We conduct experiments to demonstrate the effectiveness of the proposed model over the latest methods and provide extensive ablation studies.
