We present MultiPly, a novel framework to reconstruct multiple people in 3D
from monocular in-the-wild videos. Reconstructing multiple individuals moving
and interacting naturally from monocular in-the-wild videos poses a challenging
task. Addressing it necessitates precise pixel-level disentanglement of
individuals without any prior knowledge about the subjects. Moreover, it
requires recovering intricate and complete 3D human shapes from short video
sequences, intensifying the level of difficulty. To tackle these challenges, we
first define a layered neural representation for the entire scene, composited
by individual human and background models. We learn the layered neural
representation from videos via our layer-wise differentiable volume rendering.
This learning process is further enhanced by our hybrid instance segmentation
approach which combines the self-supervised 3D segmentation and the promptable
2D segmentation module, yielding reliable instance segmentation supervision
even under close human interaction. A confidence-guided optimization
formulation is introduced to optimize the human poses and shape/appearance
alternately. We incorporate effective objectives to refine human poses via
photometric information and impose physically plausible constraints on human
dynamics, leading to temporally consistent 3D reconstructions with high
fidelity. The evaluation of our method shows the superiority over prior art on
publicly available datasets and in-the-wild videos.

通过我的研究，我们提出了一种名为 MultiPly 的新型框架，能够从野外单目视频中重建多个人的 3D 模型。该框架利用分层神经表示来还原整个场景，其中包括个体人物和背景模型，并通过层次不可分离的体积渲染学习这一分层神经表示。我们的混合实例分割方法结合了自我监督的 3D 分割和可提示的 2D 分割模块，可以在近距离人体交互的情况下得到可靠的实例分割辅助训练。我们引入了一种基于置信度的优化公式，交替优化人体姿势和形状 / 外貌。通过利用光度信息改善人体姿势，并对人体动态施加物理可行性约束，我们达到了具有高度保真度的时间一致的 3D 重建结果。我们的方法在公开数据集和野外视频上展现了超越先前研究成果的优越性能。