  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.
