Merge pull request #29 from Lotayou/master

20190613 Update readme with toy dataset link

Author: Lotayou

PREPS.md

@@ -19,7 +19,7 @@ Make sure you have an GPU card with at least 12 GB graphic memory. You can decre
 ### Prepare dataset
 We hereby datail the necessary steps to reproduce human36m experiments. Most people failed on this step, so I hope this instruction could make sure you don't become one of them.
 
-1. Download human36m dataset from the [official website](http://vision.imar.ro/human3.6m/description.php)
+1. I tentatively suggest users to first try download human36m dataset from the [official website](http://vision.imar.ro/human3.6m/description.php). However manual authorization could take anywhere from 6 days to 6 months, so if you are keen to get things going, you can start by playing with this [toy_dataset](https://pan.baidu.com/s/1szhb9B_8n6p6CeAoPUxnhw). Extraction code: 0o95 
 
 2. Unpack downloaded zip files into a single folder (which I suggest you name it `human36m`) and put it under `path-to-your-datasets`. 
 Open `data_utils/data_washing.py`, change the `root_dir` variable in main function near line 190 to your h36m dataset path and run it to perform standard data augmentations, the washed dataset will be stored in `path-to-your-datasets/human36m_washed`.

README.md

@@ -1,5 +1,9 @@
 # densebody_pytorch
-PyTorch implementation of CloudWalk's recent paper [DenseBody](https://arxiv.org/abs/1903.10153v3)
+PyTorch implementation of CloudWalk's recent paper [DenseBody](https://arxiv.org/abs/1903.10153v3).
+
+**Note**: For most recent updates, please check out the `dev` branch.
+
+**Update on 20190613** A toy dataset has been released to facilitate the reproduction of this project. checkout [`PREPS.md`](PREPS.md) for details.
 
 ![paper teaser](teaser/teaser.jpg)
 
@@ -36,15 +40,12 @@ To explore different network architectures, checkout [`NETWORKS.md`](NETWORKS.md
         - [x] Making UV_map generation module a separate class.
     - [x] [20190413]() Prepare ground truth UV maps for washed dataset.
     - [x] [20190417]() SMPL official UV map supported!
+    - [x] [20190613]() A testing toy dataset has been released!
 
 - [x] Prepare baseline model training
     - [x] [20190414]() Network design, configs, trainer and dataloader
     - [x] [20190414]() Baseline complete with first-hand results. Something issue still needs to be addressed.
     - [x] [20190420]() Testing with different UV maps.
-    - [ ] Testing with several new loss functions.
-    
-- [ ] Report 3D reconstruction results.
-    - [ ] Setup evaluation protocal and MPJPE-PA metrics.
 
 ### Authors
 **[Lingbo Yang(Lotayou)](https://github.com/Lotayou)**: The owner and maintainer of this repo.