Reconstructing 3D human bodies from realistic motion sequences remains a
challenge due to pervasive and complex occlusions. Current methods struggle to
capture the dynamics of occluded body parts, leading to model penetration and
distorted motion. RemoCap leverages Spatial Disentanglement (SD) and Motion
Disentanglement (MD) to overcome these limitations. SD addresses occlusion
interference between the target human body and surrounding objects. It achieves
this by disentangling target features along the dimension axis. By aligning
features based on their spatial positions in each dimension, SD isolates the
target object's response within a global window, enabling accurate capture
despite occlusions. The MD module employs a channel-wise temporal shuffling
strategy to simulate diverse scene dynamics. This process effectively
disentangles motion features, allowing RemoCap to reconstruct occluded parts
with greater fidelity. Furthermore, this paper introduces a sequence velocity
loss that promotes temporal coherence. This loss constrains inter-frame
velocity errors, ensuring the predicted motion exhibits realistic consistency.
Extensive comparisons with state-of-the-art (SOTA) methods on benchmark
datasets demonstrate RemoCap's superior performance in 3D human body
reconstruction. On the 3DPW dataset, RemoCap surpasses all competitors,
achieving the best results in MPVPE (81.9), MPJPE (72.7), and PA-MPJPE (44.1)
metrics. Codes are available at this https URL

RemoCap 使用空间解缠和动作解缠方法以解决复杂遮挡对 3D 人体重建造成的困难，实现溢出物体区域的准确捕获并重建遮挡的部分。

RemoCap: 动作捕捉的解缠表示学习

RemoCap: Disentangled Representation Learning for Motion Capture

We describe a method to train a generative model with latent factors that are
(approximately) independent and localized. This means that perturbing the
latent variables affects only local regions of the synthesized image,
corresponding to objects. Unlike other unsupervised generative models, ours
enables object-centric manipulation, without requiring object-level
annotations, or any form of annotation for that matter. The key to our method
is the combination of spatial disentanglement, enforced by a Contextual
Information Separation loss, and perceptual cycle-consistency, enforced by a
loss that penalizes changes in the image partition in response to perturbations
of the latent factors. We test our method's ability to allow independent
control of spatial and semantic factors of variability on existing datasets and
also introduce two new ones that highlight the limitations of current methods.

本文提出一种训练生成模型的方法，通过强制要求上下文信息分离和知觉循环一致性的结合，让潜在变量只会影响到某个物体的局部区域，从而实现对象为中心的操作，而且不需要对象级别的注释。