Visual and spatial context fusion for implicit human reconstruction

3D human reconstruction aims to recover the 3D mesh of clothed-human from multi-view images. Recently, deep implicit function methods have won great success in this task for their detailed modeling. However, these efforts typically learn the implicit function in a point-wise manner, which ignores local context, resulting in shape artifacts. In this paper, we propose a Visual and Spatial Context fusion Implicit Function network, named VSC-IF. Specifically, we design two key modules: (i) a transformer-based encoder to model local geometry and learn global shape dependencies from images, and (ii) a feature fusion module to provide spatial context information for reconstruction. We validate our method and evaluate the generalization performance on two common datasets. Experiments show that our model achieves a new state-of-the-art performance, especially, its visual results exhibit less shape distortion and broken limbs than previous methods.