Topology-Preserved Human Reconstruction with Details
This work addresses the problem of detailed human reconstruction for computer vision applications, but it appears incremental as it combines existing paradigms.
The paper tackles the challenge of reconstructing detailed human geometry from a single image by bridging model-based and model-free approaches, resulting in an effective method validated on DeepHuman and a collected dataset.
It is challenging to directly estimate the human geometry from a single image due to the high diversity and complexity of body shapes with the various clothing styles. Most of model-based approaches are limited to predict the shape and pose of a minimally clothed body with over-smoothing surface. While capturing the fine detailed geometries, the model-free methods are lack of the fixed mesh topology. To address these issues, we propose a novel topology-preserved human reconstruction approach by bridging the gap between model-based and model-free human reconstruction. We present an end-to-end neural network that simultaneously predicts the pixel-aligned implicit surface and an explicit mesh model built by graph convolutional neural network. Experiments on DeepHuman and our collected dataset showed that our approach is effective. The code will be made publicly available.