Fitting an underlying body model to 3D clothed human assets has been extensively studied, yet most
approaches focus on either single-modal inputs such as point clouds or multi-view images alone, often
requiring known metric scale, a constraint that is frequently unavailable, particularly for AIGC-generated
assets where scale distortion is prevalent. We propose OmniFit, where "Omni" signifies our
method's ability to seamlessly handle diverse multi-modal inputs such as full scans, partial depth, and image
captures, while remaining scale-agnostic across both real and synthetic assets. Our key innovation is a simple
yet effective conditional transformer decoder that directly transforms surface points into dense body landmarks,
which are subsequently used for SMPL-X parameter fitting. Additionally, an optional plug-and-play image adapter
enriches geometric details with visual cues to address potential incompleteness. We further introduce a dedicated
scale predictor to resize subjects into canonical proportions. Remarkably, OmniFit substantially
outperforms state-of-the-art methods by 57.1%-70.9% across daily and loose
clothing scenarios, making it the first body fitting method to surpass multi-view optimization baselines and the
first to achieve millimeter-level accuracy on CAPE and 4D-DRESS benchmarks.
mniFit
