This work addresses the problem of model-based human pose estimation. Recent
approaches have made significant progress towards regressing the parameters of
parametric human body models directly from images. Because of the absence of
images with 3D shape ground truth, relevant approaches rely on 2D annotations
or sophisticated architecture designs. In this work, we advocate that there are
more cues we can leverage, which are available for free in natural images,
i.e., without getting more annotations, or modifying the network architecture.
We propose a natural form of supervision, that capitalizes on the appearance
constancy of a person among different frames (or viewpoints). This seemingly
insignificant and often overlooked cue goes a long way for model-based pose
estimation. The parametric model we employ allows us to compute a texture map
for each frame. Assuming that the texture of the person does not change
dramatically between frames, we can apply a novel texture consistency loss,
which enforces that each point in the texture map has the same texture value
across all frames. Since the texture is transferred in this common texture map
space, no camera motion computation is necessary, or even an assumption of
smoothness among frames. This makes our proposed supervision applicable in a
variety of settings, ranging from monocular video, to multi-view images. We
benchmark our approach against strong baselines that require the same or even
more annotations that we do and we consistently outperform them.
Simultaneously, we achieve state-of-the-art results among model-based pose
estimation approaches in different benchmarks. The project website with videos,
results, and code can be found at
this https URL

该论文研究了基于模型的人体姿态估计问题，提出了一种自然监督方式，并通过引入纹理一致性损失来提高估计精度。