Pyramidal Mesh Alignment Feedback
3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images.By directly mapping from raw pixels to model parameters, these methods can produce parametric models in a feed-forward manner via neural networks.However, minor deviation in parameters may lead to noticeable misalignment between the estimated meshes and imageevidences.To address this issue, we propose a pyramidal mesh alignmentfeedback (pymaf) loop to leverage a feature pyramid and rectify the predicted parameters explicitly based on the mesh-image alignment status in our deepregressor.To reduce noise and enhance the reliabilityof these evidences, an auxiliary pixel-wise supervision is imposed on the feature encoder, which provides mesh-image correspondence guidance for our network to preserve the most related information in spatial features.Experimental results show that our approach consistently improves the mesh-image alignment of the reconstruction.
