Recent progress in human shape learning, shows that neural implicit models
are effective in generating 3D human surfaces from limited number of views, and
even from a single RGB image. However, existing monocular approaches still
struggle to recover fine geometric details such as face, hands or cloth
wrinkles. They are also easily prone to depth ambiguities that result in
distorted geometries along the camera optical axis. In this paper, we explore
the benefits of incorporating depth observations in the reconstruction process
by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes
from single-view RGB-D images with an unprecedented level of accuracy. Our
model learns geometric details from both multi-resolution pixel-aligned and
voxel-aligned features to leverage depth information and enable spatial
relationships, mitigating depth ambiguities. We further enhance the quality of
the reconstructed shape by introducing a depth-supervision strategy, which
improves the accuracy of the signed distance field estimation of points that
lie on the reconstructed surface. Experiments demonstrate that ANIM outperforms
state-of-the-art works that use RGB, surface normals, point cloud or RGB-D data
as input. In addition, we introduce ANIM-Real, a new multi-modal dataset
comprising high-quality scans paired with consumer-grade RGB-D camera, and our
protocol to fine-tune ANIM, enabling high-quality reconstruction from
real-world human capture.

通过引入深度观察来重建任意 3D 人体形状的 ANIM 方法，在生成具有精确度的 3D 人体表面方面，优于使用 RGB、表面法线、点云或 RGB-D 数据的最新方法。

ANIM：从单个 RGB-D 图像准确重建人类模型的神经隐式模型

ANIM: Accurate Neural Implicit Model for Human Reconstruction from a  single RGB-D image

Direct optimization of interpolated features on multi-resolution voxel grids
has emerged as a more efficient alternative to MLP-like modules. However, this
approach is constrained by higher memory expenses and limited representation
capabilities. In this paper, we introduce a novel dynamic grid optimization
method for high-fidelity 3D surface reconstruction that incorporates both RGB
and depth observations. Rather than treating each voxel equally, we optimize
the process by dynamically modifying the grid and assigning more finer-scale
voxels to regions with higher complexity, allowing us to capture more intricate
details. Furthermore, we develop a scheme to quantify the dynamic subdivision
of voxel grid during optimization without requiring any priors. The proposed
approach is able to generate high-quality 3D reconstructions with fine details
on both synthetic and real-world data, while maintaining computational
efficiency, which is substantially faster than the baseline method NeuralRGBD.

介绍了一种新的基于动态网格优化的高清晰度 3D 表面重建方法，其中包含 RGB 和深度观测，该方法能够在维持计算效率的同时，在合成和真实数据上生成细节丰富的高质量 3D 重建。