SDCNet: Video Prediction Using Spatially-Displaced Convolution

Reda, Fitsum A.; Liu, Guilin; Shih, Kevin J.; Kirby, Robert; Barker, Jon; Tarjan, David; Tao, Andrew; Catanzaro, Bryan

doi:10.48550/arxiv.1811.00684

Cited by 1 publication

(1 citation statement)

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“…On the contrary, motion-based methods [8,9] excel in making sharp predictions, yet fail in occlusion areas where motion predictions are erroneous or ill-defined. Meanwhile, Reda et al [34] propose to model moving appearances with both convolutional kernels as in [10] and vectors as optical flow. Our closest prior work is [11] which also composes the pixel-and flow-based predictions through occlusion maps.…”

Section: High-fidelity Video Predictionmentioning

confidence: 99%

Disentangling Propagation and Generation for Video Prediction

Gao

Cai³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated. Prior approaches to video prediction typically learn either to warp or to hallucinate future pixels, but not both. In this paper, we describe a computational model for high-fidelity video prediction which disentangles motion-specific propagation from motion-agnostic generation. We introduce a confidence-aware warping operator which gates the output of pixel predictions from a flow predictor for non-occluded regions and from a context encoder for occluded regions. Moreover, in contrast to prior works where confidence is jointly learned with flow and appearance using a single network, we compute confidence after a warping step, and employ a separate network to inpaint exposed regions. Empirical results on both synthetic and real datasets show that our disentangling approach provides better occlusion maps and produces both sharper and more realistic predictions compared to strong baselines.

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