NeuHMR: Neural Rendering-Guided Human Motion Reconstruction

Reconstructing 3D human movements from video sequences is an important task in the fields of computer vision, graphics, and biomechanics. Although much progress has been made to infer 3D human mesh based on visual contexts provided in video sequences, generalization to in-the-wild videos still remains challenging for existing human mesh recovery (HMR) methods. To overcome inaccurate prediction, they can perform a second step optimization that refines the inaccurate estimations continuously at test time. Most optimization methods seek fitting of the body joints in the image space with respect to pseudo ground truth predicted by an off-the-shelf key point detector. However, state-of-theart detectors still introduce errors, especially for challenging poses. In this work, we rethink the dependency on the 2D key point fitting paradigm and present NeuHMR, an optimization-based mesh recovery framework based on recent advances in neural rendering. Our method builds on Human Neural Radiance Fields that allow the refinement of human meshes through animatable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2 D$</tex> renderings. We evaluated our method on two common benchmarks and validated its effectiveness.