Recent approaches to point tracking are able to recover the trajectory of any
scene point through a large portion of a video despite the presence of
occlusions. They are, however, too slow in practice to track every point
observed in a single frame in a reasonable amount of time. This paper
introduces DOT, a novel, simple and efficient method for solving this problem.
It first extracts a small set of tracks from key regions at motion boundaries
using an off-the-shelf point tracking algorithm. Given source and target
frames, DOT then computes rough initial estimates of a dense flow field and
visibility mask through nearest-neighbor interpolation, before refining them
using a learnable optical flow estimator that explicitly handles occlusions and
can be trained on synthetic data with ground-truth correspondences. We show
that DOT is significantly more accurate than current optical flow techniques,
outperforms sophisticated "universal" trackers like OmniMotion, and is on par
with, or better than, the best point tracking algorithms like CoTracker while
being at least two orders of magnitude faster. Quantitative and qualitative
experiments with synthetic and real videos validate the promise of the proposed
approach. Code, data, and videos showcasing the capabilities of our approach
are available in the project webpage: this https URL .

本文介绍了 DOT，一种新颖、简单且高效的方法，用于解决点追踪中存在的遮挡问题，并且通过最近邻插值计算粗糙的初始估计密集流场和可见性掩码，然后利用可学习的光流估计器对其进行精细化处理。实验证明 DOT 相较于当前的光流技术更加准确，在速度方面至少快两个数量级。

稠密光学跟踪：连接数据点

Dense Optical Tracking: Connecting the Dots

We address the problem of recovering the 3D geometry of a human face from a
set of facial images in multiple views. While recent studies have shown
impressive progress in 3D Morphable Model (3DMM) based facial reconstruction,
the settings are mostly restricted to a single view. There is an inherent
drawback in the single-view setting: the lack of reliable 3D constraints can
cause unresolvable ambiguities. We in this paper explore 3DMM-based shape
recovery in a different setting, where a set of multi-view facial images are
given as input. A novel approach is proposed to regress 3DMM parameters from
multi-view inputs with an end-to-end trainable Convolutional Neural Network
(CNN). Multiview geometric constraints are incorporated into the network by
establishing dense correspondences between different views leveraging a novel
self-supervised view alignment loss. The main ingredient of the view alignment
loss is a differentiable dense optical flow estimator that can backpropagate
the alignment errors between an input view and a synthetic rendering from
another input view, which is projected to the target view through the 3D shape
to be inferred. Through minimizing the view alignment loss, better 3D shapes
can be recovered such that the synthetic projections from one view to another
can better align with the observed image. Extensive experiments demonstrate the
superiority of the proposed method over other 3DMM methods.

本文提出一种基于卷积神经网络和多视图图像的 3D 人脸重建方法，使用自监督的视角对齐损失来减少视图之间对齐误差，并使用光流法预测 3D 形状，实现更好的 3D 重建结果。