We address the problem of synthesizing novel views from a monocular video
depicting a complex dynamic scene. State-of-the-art methods based on temporally
varying Neural Radiance Fields (aka dynamic NeRFs) have shown impressive
results on this task. However, for long videos with complex object motions and
uncontrolled camera trajectories, these methods can produce blurry or
inaccurate renderings, hampering their use in real-world applications. Instead
of encoding the entire dynamic scene within the weights of MLPs, we present a
new approach that addresses these limitations by adopting a volumetric
image-based rendering framework that synthesizes new viewpoints by aggregating
features from nearby views in a scene-motion-aware manner. Our system retains
the advantages of prior methods in its ability to model complex scenes and
view-dependent effects, but also enables synthesizing photo-realistic novel
views from long videos featuring complex scene dynamics with unconstrained
camera trajectories. We demonstrate significant improvements over
state-of-the-art methods on dynamic scene datasets, and also apply our approach
to in-the-wild videos with challenging camera and object motion, where prior
methods fail to produce high-quality renderings. Our project webpage is at
dynibar.github.io.

本篇论文提出一种基于体积图像渲染的新方法，通过汇集场景相邻视野的特征，以动态场景感知的方式合成新视点，从而在长视频中合成具有真实感的新视图，能够更好地应对复杂的场景动力学和不受限约束的相机轨迹。

DynIBaR: 神经动态图像渲染

DynIBaR: Neural Dynamic Image-Based Rendering

We present an approach to predict future video frames given a sequence of
continuous video frames in the past. Instead of synthesizing images directly,
our approach is designed to understand the complex scene dynamics by decoupling
the background scene and moving objects. The appearance of the scene components
in the future is predicted by non-rigid deformation of the background and
affine transformation of moving objects. The anticipated appearances are
combined to create a reasonable video in the future. With this procedure, our
method exhibits much less tearing or distortion artifact compared to other
approaches. Experimental results on the Cityscapes and KITTI datasets show that
our model outperforms the state-of-the-art in terms of visual quality and
accuracy.

利用背景场景和移动物体的非刚性变形和仿射变换对未来视频帧进行预测，实现较低的变形失真，并在 Cityscapes 和 KITTI 数据集上呈现更高的视觉质量和准确性。