2D keypoints are commonly used as an additional cue to refine estimated 3D
human meshes. Current methods optimize the pose and shape parameters with a
reprojection loss on the provided 2D keypoints. Such an approach, while simple
and intuitive, has limited effectiveness because the optimal solution is hard
to find in ambiguous parameter space and may sacrifice depth. Additionally,
divergent gradients from distal joints complicate and deviate the refinement of
proximal joints in the kinematic chain. To address these, we introduce
Kinematic-Tree Rotation (KITRO), a novel mesh refinement strategy that
explicitly models depth and human kinematic-tree structure. KITRO treats
refinement from a bone-wise perspective. Unlike previous methods which perform
gradient-based optimizations, our method calculates bone directions in closed
form. By accounting for the 2D pose, bone length, and parent joint's depth, the
calculation results in two possible directions for each child joint. We then
use a decision tree to trace binary choices for all bones along the human
skeleton's kinematic-tree to select the most probable hypothesis. Our
experiments across various datasets and baseline models demonstrate that KITRO
significantly improves 3D joint estimation accuracy and achieves an ideal 2D
fit simultaneously. Our code available at: this https URL

通过具体模型人深度和人体运动树结构，Kinematic-Tree Rotation (KITRO) 方法以基于骨结构为视角进行网格精化，从而显著提高 3D 关节估计准确性，并同时实现理想的 2D 匹配。

KITRO：通过 2D 线索和运动树旋转细化人体网格

KITRO: Refining Human Mesh by 2D Clues and Kinematic-tree Rotation

This research presents the development of an innovative algorithm tailored
for the adaptive sampling of residual points within the framework of
Physics-Informed Neural Networks (PINNs). By addressing the limitations
inherent in existing adaptive sampling techniques, our proposed methodology
introduces a direct mesh refinement approach that effectively ensures both
computational efficiency and adaptive point placement. Verification studies
were conducted to evaluate the performance of our algorithm, showcasing
reasonable agreement between the model based on our novel approach and
benchmark model results. Comparative analyses with established adaptive
resampling techniques demonstrated the superior performance of our approach,
particularly when implemented with higher refinement factor. Overall, our
findings highlight the enhancement of simulation accuracy achievable through
the application of our adaptive sampling algorithm for Physics-Informed Neural
Networks.

开发了一种适用于物理信息神经网络中残余点的自适应采样的创新算法，具有直接网格细化方法和高效的计算方法，能够提高模拟精度和性能。

使用直接网格细化算法高效训练物理知情神经网络

Efficient Training of Physics-Informed Neural Networks with Direct Grid  Refinement Algorithm

All hand-object interaction is controlled by forces that the two bodies exert
on each other, but little work has been done in modeling these underlying
forces when doing pose and contact estimation from RGB/RGB-D data. Given the
pose of the hand and object from any pose estimation system, we propose an
end-to-end differentiable model that refines pose estimates by learning the
forces experienced by the object at each vertex in its mesh. By matching the
learned net force to an estimate of net force based on finite differences of
position, this model is able to find forces that accurately describe the
movement of the object, while resolving issues like mesh interpenetration and
lack of contact. Evaluating on the ContactPose dataset, we show this model
successfully corrects poses and finds contact maps that better match the ground
truth, despite not using any RGB or depth image data.

本文提出了一种端到端可微模型，通过学习目标网格形状中每个顶点处的受力情况来修正手和物体的姿态，从而实现更准确的接触估计和姿态估计。

使用完全可微分的力物理合理的姿态细化

Physically Plausible Pose Refinement using Fully Differentiable Forces

While 3D reconstruction is a well-established and widely explored research
topic, semantic 3D reconstruction has only recently witnessed an increasing
share of attention from the Computer Vision community. Semantic annotations
allow in fact to enforce strong class-dependent priors, as planarity for ground
and walls, which can be exploited to refine the reconstruction often resulting
in non-trivial performance improvements. State-of-the art methods propose
volumetric approaches to fuse RGB image data with semantic labels; even if
successful, they do not scale well and fail to output high resolution meshes.
In this paper we propose a novel method to refine both the geometry and the
semantic labeling of a given mesh. We refine the mesh geometry by applying a
variational method that optimizes a composite energy made of a state-of-the-art
pairwise photo-metric term and a single-view term that models the semantic
consistency between the labels of the 3D mesh and those of the segmented
images. We also update the semantic labeling through a novel Markov Random
Field (MRF) formulation that, together with the classical data and smoothness
terms, takes into account class-specific priors estimated directly from the
annotated mesh. This is in contrast to state-of-the-art methods that are
typically based on handcrafted or learned priors. We are the first, jointly
with the very recent and seminal work of [M. Blaha et al arXiv:1706.08336,
2017], to propose the use of semantics inside a mesh refinement framework.
Differently from [M. Blaha et al arXiv:1706.08336, 2017], which adopts a more
classical pairwise comparison to estimate the flow of the mesh, we apply a
single-view comparison between the semantically annotated image and the current
3D mesh labels; this improves the robustness in case of noisy segmentations.

本文提出了一种新颖的方法，通过应用变分方法和 MRF 来细化给定网格的几何和语义标注，并针对通过半监督学习直接估计类特定先验的方法进行了改进。