DeepAI AI Chat
Log In Sign Up

Detailed 3D Human Body Reconstruction from Multi-view Images Combining Voxel Super-Resolution and Learned Implicit Representation

by   Zhongguo Li, et al.

The task of reconstructing detailed 3D human body models from images is interesting but challenging in computer vision due to the high freedom of human bodies. In order to tackle the problem, we propose a coarse-to-fine method to reconstruct a detailed 3D human body from multi-view images combining voxel super-resolution based on learning the implicit representation. Firstly, the coarse 3D models are estimated by learning an implicit representation based on multi-scale features which are extracted by multi-stage hourglass networks from the multi-view images. Then, taking the low resolution voxel grids which are generated by the coarse 3D models as input, the voxel super-resolution based on an implicit representation is learned through a multi-stage 3D convolutional neural network. Finally, the refined detailed 3D human body models can be produced by the voxel super-resolution which can preserve the details and reduce the false reconstruction of the coarse 3D models. Benefiting from the implicit representation, the training process in our method is memory efficient and the detailed 3D human body produced by our method from multi-view images is the continuous decision boundary with high-resolution geometry. In addition, the coarse-to-fine method based on voxel super-resolution can remove false reconstructions and preserve the appearance details in the final reconstruction, simultaneously. In the experiments, our method quantitatively and qualitatively achieves the competitive 3D human body reconstructions from images with various poses and shapes on both the real and synthetic datasets.


page 15

page 19

page 22


Super-NeRF: View-consistent Detail Generation for NeRF super-resolution

The neural radiance field (NeRF) achieved remarkable success in modeling...

FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction

Recent works on 3D reconstruction from posed images have demonstrated th...

Occupancy Networks: Learning 3D Reconstruction in Function Space

With the advent of deep neural networks, learning-based approaches for 3...

Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution

Recent multi-view multimedia applications struggle between high-resoluti...

Improving Multi-View Stereo via Super-Resolution

Today, Multi-View Stereo techniques are able to reconstruct robust and d...

SeSDF: Self-evolved Signed Distance Field for Implicit 3D Clothed Human Reconstruction

We address the problem of clothed human reconstruction from a single ima...

VIINTER: View Interpolation with Implicit Neural Representations of Images

We present VIINTER, a method for view interpolation by interpolating the...