We propose a new representation of visual data that disentangles object
position from appearance. Our method, termed Deep Latent Particles (DLP),
decomposes the visual input into low-dimensional latent ``particles'', where
each particle is described by its spatial location and features of its
surrounding region. To drive learning of such representations, we follow a
VAE-based approach and introduce a prior for particle positions based on a
spatial-softmax architecture, and a modification of the evidence lower bound
loss inspired by the Chamfer distance between particles. We demonstrate that
our DLP representations are useful for downstream tasks such as unsupervised
keypoint (KP) detection, image manipulation, and video prediction for scenes
composed of multiple dynamic objects. In addition, we show that our
probabilistic interpretation of the problem naturally provides uncertainty
estimates for particle locations, which can be used for model selection, among
other tasks. Videos and code are available:
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提出一种新的视觉数据表示方法 Deep Latent Particles (DLP)，其中用空间 - softmax 和 Chamfer 距离先验驱动学习生成低维潜在颗粒表示，并表明其对多个动态对象构成的场景视频预测等下游任务具有实用性，同时给出颗粒位置的不确定性估计。