To date, little attention has been given to multi-view 3D human mesh
estimation, despite real-life applicability (e.g., motion capture, sport
analysis) and robustness to single-view ambiguities. Existing solutions
typically suffer from poor generalization performance to new settings, largely
due to the limited diversity of image-mesh pairs in multi-view training data.
To address this shortcoming, people have explored the use of synthetic images.
But besides the usual impact of visual gap between rendered and target data,
synthetic-data-driven multi-view estimators also suffer from overfitting to the
camera viewpoint distribution sampled during training which usually differs
from real-world distributions. Tackling both challenges, we propose a novel
simulation-based training pipeline for multi-view human mesh recovery, which
(a) relies on intermediate 2D representations which are more robust to
synthetic-to-real domain gap; (b) leverages learnable calibration and
triangulation to adapt to more diversified camera setups; and (c) progressively
aggregates multi-view information in a canonical 3D space to remove ambiguities
in 2D representations. Through extensive benchmarking, we demonstrate the
superiority of the proposed solution especially for unseen in-the-wild
scenarios.

通过使用中间的二维表示，可学习的校准和三角测量来适应更多样化的相机设置，逐步聚合多视图信息在规范化三维空间中消除二维表示中的歧义，通过广泛的基准测试，特别是针对野外场景的未见过的情况下，我们展示了所提出的解决方案的优越性。

具备自监督的渐进式多视角人体网格恢复

Progressive Multi-view Human Mesh Recovery with Self-Supervision

Fully supervised human mesh recovery methods are data-hungry and have poor
generalizability due to the limited availability and diversity of 3D-annotated
benchmark datasets. Recent progress in self-supervised human mesh recovery has
been made using synthetic-data-driven training paradigms where the model is
trained from synthetic paired 2D representation (e.g., 2D keypoints and
segmentation masks) and 3D mesh. However, on synthetic dense correspondence
maps (i.e., IUV) few have been explored since the domain gap between synthetic
training data and real testing data is hard to address for 2D dense
representation. To alleviate this domain gap on IUV, we propose
cross-representation alignment utilizing the complementary information from the
robust but sparse representation (2D keypoints). Specifically, the alignment
errors between initial mesh estimation and both 2D representations are
forwarded into regressor and dynamically corrected in the following mesh
regression. This adaptive cross-representation alignment explicitly learns from
the deviations and captures complementary information: robustness from sparse
representation and richness from dense representation. We conduct extensive
experiments on multiple standard benchmark datasets and demonstrate competitive
results, helping take a step towards reducing the annotation effort needed to
produce state-of-the-art models in human mesh estimation.

本文介绍了一种针对现有限制的新型人体网格恢复方法，旨在优化存在的域间差距和数据不对称问题，并通过对多个标准数据集的实验验证，取得了与现有模型相媲美的成果，从而进一步降低了人体网格估计所需的注释工作量。