When perceiving the world from multiple viewpoints, humans have the ability to reason about the complete objects in a compositional manner even when the object is completely occluded from partial viewpoints. Meanwhile, humans can imagine the novel views after observing multiple viewpoints. The remarkable recent advance in multi-view object-centric learning leaves some problems: 1) the partially or completely occluded shape of objects can not be well reconstructed. 2) the novel viewpoint prediction depends on expensive viewpoint annotations rather than implicit view rules. This makes the agent fail to perform like humans. In this paper, we introduce a time-conditioned generative model for videos. To reconstruct the complete shape of the object accurately, we enhance the disentanglement between different latent representations: view latent representations are jointly inferred based on the Transformer and then cooperate with the sequential extension of Slot Attention to learn object-centric representations. The model also achieves the new ability: Gaussian processes are employed as priors of view latent variables for generation and novel-view prediction without viewpoint annotations. Experiments on multiple specifically designed synthetic datasets have shown that the proposed model can 1) make the video decomposition, 2) reconstruct the complete shapes of objects, and 3) make the novel viewpoint prediction without viewpoint annotations.

翻译：当人类从多个视角感知世界时，即使物体在部分视角下被完全遮挡，也能以组合方式推理出完整物体。同时，人类在观察多个视角后能够想象出新视角的图像。近年来多视角对象中心学习的研究取得了显著进展，但依然存在以下问题：1) 物体部分或完全被遮挡的形状无法得到良好重建；2) 新视角预测依赖昂贵的视角标注，而非隐式视角规则。这使得智能体无法像人类一样执行任务。本文提出一种基于时间条件的视频生成模型。为精确重建物体完整形状，我们增强了不同潜在表示之间的解耦性：基于Transformer联合推断视角潜在表示，并与Slot Attention的时序扩展协作学习对象中心表示。该模型还实现了新能力：采用高斯过程作为视角潜在变量的先验，无需视角标注即可生成图像并预测新视角。在多个专门设计的合成数据集上的实验表明，所提模型能够：1) 实现视频分解；2) 重建物体完整形状；3) 无需视角标注即可进行新视角预测。