Object-Centric Representation Learning with Generative Spatial-Temporal FactorizationDownload PDF

21 May 2021, 20:46 (modified: 22 Jan 2022, 00:59)NeurIPS 2021 PosterReaders: Everyone
Keywords: Object-centric Representation Learning, Deep Generative Models, Computer Vision
TL;DR: We extend the idea of unsupervised object-centric representation learning to multi-view-dynamic-scene settings and show that our method can perform novel-view synthesis and segmentation prediction for different times.
Abstract: Learning object-centric scene representations is essential for attaining structural understanding and abstraction of complex scenes. Yet, as current approaches for unsupervised object-centric representation learning are built upon either a stationary observer assumption or a static scene assumption, they often: i) suffer single-view spatial ambiguities, or ii) infer incorrectly or inaccurately object representations from dynamic scenes. To address this, we propose Dynamics-aware Multi-Object Network (DyMON), a method that broadens the scope of multi-view object-centric representation learning to dynamic scenes. We train DyMON on multi-view-dynamic-scene data and show that DyMON learns---without supervision---to factorize the entangled effects of observer motions and scene object dynamics from a sequence of observations, and constructs scene object spatial representations suitable for rendering at arbitrary times (querying across time) and from arbitrary viewpoints (querying across space). We also show that the factorized scene representations (w.r.t. objects) support querying about a single object by space and time independently.
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