Novel view synthesis from a single image has recently attracted a lot of
attention, and it has been primarily advanced by 3D deep learning and rendering
techniques. However, most work is still limited by synthesizing new views
within relatively small camera motions. In this paper, we propose a novel
approach to synthesize a consistent long-term video given a single scene image
and a trajectory of large camera motions. Our approach utilizes an
autoregressive Transformer to perform sequential modeling of multiple frames,
which reasons the relations between multiple frames and the corresponding
cameras to predict the next frame. To facilitate learning and ensure
consistency among generated frames, we introduce a locality constraint based on
the input cameras to guide self-attention among a large number of patches
across space and time. Our method outperforms state-of-the-art view synthesis
approaches by a large margin, especially when synthesizing long-term future in
indoor 3D scenes. Project page at this https URL

在这篇论文中，我们提出了一种新的方法来从单个场景图像和大摄像机动作轨迹中综合出一致的长视频，并使用自回归 Transformer 以及基于摄像机的局部性约束来预测多个帧之间关系和下一帧，从而实现了对多帧的序列建模，优于现有的方法，尤其是在室内 3D 场景的长期未来合成中表现出色。

从单张图片合成一致的长期 3D 场景视频

Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene  Video from A Single Image

Locally-biased graph algorithms are algorithms that attempt to find local or
small-scale structure in a large data graph. In some cases, this can be
accomplished by adding some sort of locality constraint and calling a
traditional graph algorithm; but more interesting are locally-biased graph
algorithms that compute answers by running a procedure that does not even look
at most of the input graph. This corresponds more closely to what practitioners
from various data science domains do, but it does not correspond well with the
way that algorithmic and statistical theory is typically formulated. Recent
work from several research communities has focused on developing locally-biased
graph algorithms that come with strong complementary algorithmic and
statistical theory and that are useful in practice in downstream data science
applications. We provide a review and overview of this work, highlighting
commonalities between seemingly-different approaches, and highlighting
promising directions for future work.

本文综述了最近几年来几个研究社区对局部偏差图算法的发展和应用，以及这些算法和算法论和统计理论的相互关系。