I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image

News

🎉 Our I2L-MeshNet wons the first and second place at 3DPW challenge on unknown assocation track in part orientation and joint position metrics, respectively. 🎉

Introduction

This repo is official PyTorch implementation of I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image (ECCV 2020).

Quick demo

You can try quick demo at demo folder.

Download the pre-trained I2L-MeshNet in here.
Prepare input.jpg and pre-trained snapshot at demo folder.
Download basicModel_neutral_lbs_10_207_0_v1.0.0.pkl at common/utils/smplpytorch/smplpytorch/native/models from here.
Run python demo.py --gpu 0 --stage param --test_epoch 7 if you want to run on gpu 0.
You can see output_mesh.jpg and output_mesh.obj.

Directory

Root

The ${ROOT} is described as below.

${ROOT}  
|-- data  
|-- demo
|-- common  
|-- main  
|-- output

data contains data loading codes and soft links to images and annotations directories.
demo contains demo codes.
common contains kernel codes for I2L-MeshNet.
main contains high-level codes for training or testing the network.
output contains log, trained models, visualized outputs, and test result.

Data

You need to follow directory structure of the data as below.

${ROOT}  
|-- data  
|   |-- Human36M  
|   |-- |-- rootnet_output  
|   |   |   |-- bbox_root_human36m_output.json  
|   |   |-- images  
|   |   |-- annotations   
|   |   |-- J_regressor_h36m_correct.npy
|   |-- MuCo  
|   |   |-- data  
|   |   |   |-- augmented_set  
|   |   |   |-- unaugmented_set  
|   |   |   |-- MuCo-3DHP.json
|   |   |   |-- smpl_param.json
|   |-- MSCOCO  
|   |   |-- rootnet_output  
|   |   |   |-- bbox_root_coco_output.json  
|   |   |-- images  
|   |   |   |-- train2017  
|   |   |   |-- val2017  
|   |   |-- annotations  
|   |   |-- J_regressor_coco_hip_smpl.npy
|   |-- PW3D
|   |   |-- rootnet_output  
|   |   |   |-- bbox_root_pw3d_output.json  
|   |   |-- data
|   |   |   |-- 3DPW_train.json
|   |   |   |-- 3DPW_validation.json
|   |   |   |-- 3DPW_test.json
|   |   |-- imageFiles
|   |-- FreiHAND
|   |   |-- rootnet_output  
|   |   |   |-- bbox_root_freihand_output.json  
|   |   |-- data
|   |   |   |-- training
|   |   |   |-- evaluation
|   |   |   |-- freihand_train_coco.json
|   |   |   |-- freihand_train_data.json
|   |   |   |-- freihand_eval_coco.json
|   |   |   |-- freihand_eval_data.json

Download Human3.6M parsed data and SMPL parameters [data][SMPL parameters from SMPLify-X]
Download MuCo parsed/composited data and SMPL parameters [data][SMPL parameters from SMPLify-X]
Download MS COCO SMPL parameters [SMPL parameters from SMPLify-X]
Download 3DPW parsed data [data]
Download FreiHAND parsed data [data] (bbox in freihand_eval_coco.json is from Detectron2)
All annotation files follow MS COCO format.
If you want to add your own dataset, you have to convert it to MS COCO format.

If you have a problem with 'Download limit' problem when tried to download dataset from google drive link, please try this trick.

* Go the shared folder, which contains files you want to copy to your drive  
* Select all the files you want to copy  
* In the upper right corner click on three vertical dots and select “make a copy”  
* Then, the file is copied to your personal google drive account. You can download it from your personal account.

Pytorch SMPL and MANO layer

For the SMPL layer, I used smplpytorch. The repo is already included in common/utils/smplpytorch.
Download basicModel_f_lbs_10_207_0_v1.0.0.pkl, basicModel_m_lbs_10_207_0_v1.0.0.pkl, and basicModel_neutral_lbs_10_207_0_v1.0.0.pkl at common/utils/smplpytorch/smplpytorch/native/models from here.
For the MANO layer, I used manopth. The repo is already included in common/utils/manopth.
Download MANO_LEFT.pkl and MANO_RIGHT.pkl at common/utils/manopth/mano/models from here.

Output

You need to follow the directory structure of the output folder as below.

${ROOT}  
|-- output  
|-- |-- log  
|-- |-- model_dump  
|-- |-- result  
|-- |-- vis

Creating output folder as soft link form is recommended instead of folder form because it would take large storage capacity.
log folder contains training log file.
model_dump folder contains saved checkpoints for each epoch.
result folder contains final estimation files generated in the testing stage.
vis folder contains visualized results.

Running I2L-MeshNet

Start

In the main/config.py, you can change settings of the model including dataset to use, network backbone, and input size and so on.
There are two stages. 1) lixel and 2) param. In the lixel stage, I2L-MeshNet predicts lixel-based 1D heatmaps for each human joint and mesh vertex. In param stage, I2L-MeshNet predicts SMPL parameters from lixel-based 1D heatmaps.

Train

1. lixel stage

First, you need to train I2L-MeshNet of lixel stage. In the main folder, run

python train.py --gpu 0-3 --stage lixel

to train I2L-MeshNet in the lixel stage on the GPU 0,1,2,3. --gpu 0,1,2,3 can be used instead of --gpu 0-3.

2. param stage

Once you pre-trained I2L-MeshNet in lixel stage, you can resume training in param stage. In the main folder, run

python train.py --gpu 0-3 --stage param --continue

to train I2L-MeshNet in the param stage on the GPU 0,1,2,3. --gpu 0,1,2,3 can be used instead of --gpu 0-3.

Test

Place trained model at the output/model_dump/. Choose the stage you want to test among lixel and param.

In the main folder, run

python test.py --gpu 0-3 --stage $STAGE --test_epoch 20

to test I2L-MeshNet in $STAGE stage (should be one of lixel and param) on the GPU 0,1,2,3 with 20th epoch trained model. --gpu 0,1,2,3 can be used instead of --gpu 0-3.

Results

Here I report the performance of the I2L-MeshNet.

Human3.6M dataset

Download I2L-MeshNet trained on [Human3.6M+MSCOCO].
Download bounding boxs and root joint coordinates (from RootNet) of [Human3.6M].

$ python test.py --gpu 4-7 --stage param --test_epoch 17
>>> Using GPU: 4,5,6,7
Stage: param
08-10 00:25:56 Creating dataset...
creating index...
index created!
Get bounding box and root from ../data/Human36M/rootnet_output/bbox_root_human36m_output.json
08-10 00:26:16 Load checkpoint from /home/mks0601/workspace/I2L-MeshNet/main/../output/model_dump/snapshot_17.pth.tar
08-10 00:26:16 Creating graph...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:46<00:00,  1.09it/s]
MPJPE from lixel mesh: 55.83 mm
PA MPJPE from lixel mesh: 41.68 mm
MPJPE from param mesh: 66.05 mm
PA MPJPE from param mesh: 46.68 mm

3DPW dataset

Download I2L-MeshNet trained on [Human3.6M+MuCo+MSCOCO].
Download bounding boxs and root joint coordinates (from RootNet) of [3DPW].

$ python test.py --gpu 4-7 --stage param --test_epoch 7
>>> Using GPU: 4,5,6,7
Stage: param
08-09 20:47:19 Creating dataset...
loading annotations into memory...
Done (t=4.91s)
creating index...
index created!
Get bounding box and root from ../data/PW3D/rootnet_output/bbox_root_pw3d_output.json
08-09 20:47:27 Load checkpoint from ../output/model_dump/snapshot_7.pth.tar
08-09 20:47:27 Creating graph...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 555/555 [08:05<00:00,  1.06s/it]
MPJPE from lixel mesh: 93.15 mm
PA MPJPE from lixel mesh: 58.50 mm
MPJPE from param mesh: 100.04 mm
PA MPJPE from param mesh: 60.74 mm

MSCOCO dataset

The testing results on MSCOCO dataset are used for visualization (qualitative results).

Download I2L-MeshNet trained on [Human3.6M+MuCo+MSCOCO].
Download bounding boxs and root joint coordinates (from RootNet) [MSCOCO].

$ python test.py --gpu 4-7 --stage param --test_epoch 7
>>> Using GPU: 4,5,6,7
Stage: param
08-10 00:34:26 Creating dataset...
loading annotations into memory...
Done (t=0.35s)
creating index...
index created!
Load RootNet output from  ../data/MSCOCO/rootnet_output/bbox_root_coco_output.json
08-10 00:34:39 Load checkpoint from /home/mks0601/workspace/I2L-MeshNet/main/../output/model_dump/snapshot_7.pth.tar
08-10 00:34:39 Creating graph...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [01:31<00:00,  1.05it/s]

FreiHAND dataset

Download I2L-MeshNet trained on [FreiHand].
Download bounding boxs and root joint coordinates (from RootNet) of [FreiHAND].

$ python test.py --gpu 4-7 --stage lixel --test_epoch 24
>>> Using GPU: 4,5,6,7
Stage: lixel
08-09 21:31:30 Creating dataset...
loading annotations into memory...
Done (t=0.06s)
creating index...
index created!
Get bounding box and root from ../data/FreiHAND/rootnet_output/bbox_root_freihand_output.json
08-09 21:31:30 Load checkpoint from ../output/model_dump/snapshot_24.pth.tar
08-09 21:31:30 Creating graph...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 62/62 [00:54<00:00,  1.12it/s]
Saved at ../output/result/pred.json

Reference

@InProceedings{Moon_2020_ECCV_I2L-MeshNet,  
author = {Moon, Gyeongsik and Lee, Kyoung Mu},  
title = {I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image},  
booktitle = {European Conference on Computer Vision (ECCV)},  
year = {2020}  
}

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