This paper presents a new method to synthesize an image from arbitrary views
and times given a collection of images of a dynamic scene. A key challenge for
the novel view synthesis arises from dynamic scene reconstruction where
epipolar geometry does not apply to the local motion of dynamic contents. To
address this challenge, we propose to combine the depth from single view (DSV)
and the depth from multi-view stereo (DMV), where DSV is complete, i.e., a
depth is assigned to every pixel, yet view-variant in its scale, while DMV is
view-invariant yet incomplete. Our insight is that although its scale and
quality are inconsistent with other views, the depth estimation from a single
view can be used to reason about the globally coherent geometry of dynamic
contents. We cast this problem as learning to correct the scale of DSV, and to
refine each depth with locally consistent motions between views to form a
coherent depth estimation. We integrate these tasks into a depth fusion network
in a self-supervised fashion. Given the fused depth maps, we synthesize a
photorealistic virtual view in a specific location and time with our deep
blending network that completes the scene and renders the virtual view. We
evaluate our method of depth estimation and view synthesis on diverse
real-world dynamic scenes and show the outstanding performance over existing
methods.

本文提出了一种新的方法，可以从动态场景的一组图像中合成任意视角和时间的图像，通过单视图深度（DSV）和多视图立体深度（DMV）的结合，重新估计视角和时间下的一致性和深度信息，从而精确的合成真实感极强的虚拟视图。