What does this research mean for the field?

Integrating pixel-aligned dense pseudo-normal maps into SMPL optimization significantly improves 3D human pose accuracy and shape realism by reducing the depth ambiguity inherent in sparse 2D cues. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to enhance 3D human body reconstruction by reducing pose estimation errors using dense normal maps.

June 6, 2026

DenseSMPLify: 3D Human Body Parametric Reconstruction Using Pixel Aligned Dense Normal Maps

Puntos clave

This research aims to enhance 3D human body reconstruction by reducing pose estimation errors using dense normal maps.
Developed DenseSMPLify as an optimization framework leveraging dense pseudo-normal maps.
Implemented an alignment algorithm based on Continuous Surface Embedding to match normal maps.
Conducted experiments comparing DenseSMPLify with SMPLify and other optimization methods.
DenseSMPLify outperformed SMPLify in pose accuracy with a notable reduction in estimation errors.
Significant improvements in shape realism were observed during 3D mesh recovery.
Enhanced pose estimates were achieved, even when starting from state-of-the-art regression models.

Resumen

Abstract Recent advances in 3D human mesh recovery from monocular images primarily rely on optimizing the SMPL model using sparse 2D cues such as 2D key points or silhouettes. However, these constraints often lead to incorrect 3D pose estimations due to inherent depth ambiguity. To address this fundamental limitation, we propose DenseSMPLify, a novel optimization framework that leverages dense pseudo‐normal maps estimated by Sapiens as complementary geometric guidance. Our key insight is that normal maps encode richer 3D shape and depth cues than sparse 2D joints, significantly reducing ambiguity during optimization. A critical challenge lies in the misalignment between SMPL‐derived normals and pseudo‐normal maps. We address this via an alignment algorithm based on Continuous Surface Embedding (CSE), which establishes pixel‐level correspondence between the two normal maps before error computation. Experiments demonstrate that DenseSMPLify outperforms SMPLify and other optimization baselines in both pose accuracy and shape realism. Notably, our method consistently improves upon initial poses, even when initialized with state‐of‐the‐art regression models.

Me gusta

Guardar