3D Body Reconstruction Revisited: Exploring the Test-time 3D Body Mesh Refinement Strategy via Surrogate Adaptation

초록

Recent 3D body reconstruction works are achieving state-of-the-art performances. Each of the prior works applied specific modifications to their respective modules, allowing the ability to show plausible predicted 3D body poses and shapes to human eyes. Unfortunately, those outputs may sometimes be far from the correct position. In contrast to these works, we took a different perspective on how to re-improve this limitation. Without any addition or modification at the module level, we propose a test-time adaptation strategy that fine-tunes the module directly. Our approach is inspired by the science of vaccination that leverages surrogate information, which is helpful and not harmful in improving the human immune system. This notion is translated to our adaptation strategy by fine-tuning the surrogate 3D body module using reliable virtual data. In doing so, the proposed work can revisit the prior state-of-the-art works and improve their performances directly in the test phase. The experimental results demonstrate our strategy's ability to straightforwardly improve the prior works, even with fast adaptation capability.

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