VMarker-Pro: Probabilistic 3D Human Mesh Estimation from Virtual Markers
arxiv(2023)
摘要
Monocular 3D human mesh estimation faces challenges due to depth ambiguity
and the complexity of mapping images to complex parameter spaces. Recent
methods propose to use 3D poses as a proxy representation, which often lose
crucial body shape information, leading to mediocre performance. Conversely,
advanced motion capture systems, though accurate, are impractical for
markerless wild images. Addressing these limitations, we introduce an
innovative intermediate representation as virtual markers, which are learned
from large-scale mocap data, mimicking the effects of physical markers.
Building upon virtual markers, we propose VMarker, which detects virtual
markers from wild images, and the intact mesh with realistic shapes can be
obtained by simply interpolation from these markers. To address occlusions that
obscure 3D virtual marker estimation, we further enhance our method with
VMarker-Pro, a probabilistic framework that generates multiple plausible meshes
aligned with images. This framework models the 3D virtual marker estimation as
a conditional denoising process, enabling robust 3D mesh estimation. Our
approaches surpass existing methods on three benchmark datasets, particularly
demonstrating significant improvements on the SURREAL dataset, which features
diverse body shapes. Additionally, VMarker-Pro excels in accurately modeling
data distributions, significantly enhancing performance in occluded scenarios.
Code and models are available at https://github.com/ShirleyMaxx/VMarker-Pro.
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