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Tensorflow implementation for paper Dense Human Body Correspondences Using Convolutional Networks.
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README.md

Dense Human Body Correspondences

This is a tensorflow implementation for paper Dense Human Body Correspondences Using Convolutional Networks.

ATTENTION: This repo is currently semi-finished, in next few weeks, the newest:

  • visualize scripts
  • training tutorial

will be updated.

Installation

  1. Clone this repository to your computer.
  2. Modify project_dir in config.py to the path of this repo.
  3. Download 3D human model meshes data.zip (48M), unzip it to the repo directory. The structure should be like path/to/repo/data/...
  4. Download pretrained model alex-SM-5 (121M), unzip it to the models directory. The structure should be like path/to/repo/models/alex-SM-5/..

Usage

Predict feature for depth scan

For input depth image with shape [H, W, 1], outputs [H, W, 16] numpy array. Example:

python predict.py --checkpoint ./models/alex-SM-5/model --output feature.npy --depth ./test.png

Predict feature for 3D human mesh

For input human mesh model (support obj and ply), outputs [vertex_count, 16] numpy array. The input mesh will be format as 1.8m tall, zero-centerd.

python predict.py --checkpoint ./models/alex-SM-5/model --output feature.npy --mesh ./test.obj --flipyz
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