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TRACE's code for inference & evaluation

[Project Page] [Paper] [Video]

To run the inference & evaluation, please first install simple-romp following the guidance.

Please refer to this instruction for training.

Installation

CAUTIONS: simple-romp for ROMP and BEV has been tested on Linux, Mac, and Windows, but its TRACE version only works in Linu x environment with CUDA support. Please make sure that you have CUDA and corresponding pytorch installed before using simple-romp for TRACE.

  1. Installing simple_romp via pip:
pip install --upgrade setuptools numpy cython lap
#download the package and install it from source:
git clone https://github.com/Arthur151/ROMP
cd simple_romp
python setup_trace.py install
  1. Preparing SMPL model files in our format:

Firstly, please register and download:
a. Meta data from this link. Please unzip it, then we get a folder named "smpl_model_data" b. SMPL model file (SMPL_NEUTRAL.pkl) from "Download version 1.1.0 for Python 2.7 (female/male/neutral, 300 shape PCs)" in official website. Please unzip it and move the SMPL_NEUTRAL.pkl from extracted folder into the "smpl_model_data" folder.
c. (Optional) If you use BEV, please also download SMIL model file (DOWNLOAD SMIL) from official website. Please unzip and put it into the "smpl_model_data" folder, so we have "smpl_model_data/smil/smil_web.pkl".
Then we can get a folder in structure like this:

|-- smpl_model_data
|   |-- SMPL_NEUTRAL.pkl
|   |-- J_regressor_extra.npy
|   |-- J_regressor_h36m.npy
|   |-- smpl_kid_template.npy
|   |-- smil
|   |-- |-- smil_web.pkl

Secondly, please convert the SMPL model files to our format via

# please provide the absolute path of the "smpl_model_data" folder to the source_dir 
romp.prepare_smpl -source_dir=/path/to/smpl_model_data
# (Optional) If you use BEV, please also run:
bev.prepare_smil -source_dir=/path/to/smpl_model_data

The converted file would be save to "~/.romp/" in defualt.

Inference

After installation, please run

# current code does not support inference with multiple GPUs.
CUDA_VISIBLE_DEVICES=0 trace2 -i /path/to/video_sequence --subject_num=N

For example, you can download our demo videos1 and videos2. Please unzip them and specify the path to these folders to run, like:

CUDA_VISIBLE_DEVICES=0 trace2 -i /path/to/trace_demo --subject_num=1
CUDA_VISIBLE_DEVICES=0 trace2 -i /path/to/trace_demo2 --subject_num=2

Optional functions:

# By default,  subject_num is set to 1, which means we only track and recover the 3D person with the largest scale in the first frame. To change it, please specify:
--subject_num=N

# By default, dir to save the results would be ~/TRACE_results. To change it, please specify:
--save_path /path/to/save/folder

# To save the rendering results in camera coordinates, please add:
--save_video

Visualization

To visualize the estimated results, please download the codes and run

cd simple_romp
python -m trace2.show --smpl_model_folder /path/to/smpl_model_data --preds_path /path/to/trace_demo.npz --frame_dir /path/to/trace_demo 
#    --smpl_model_folder   Folder contains SMPL_NEUTRAL.pkl, like /path/to/smpl_model_data
#    --preds_path          Path to save the .npz results, like /path/to/trace_demo.npz
#    --frame_dir           Path to folder of input video frames, like /path/to/trace_demo

TRACE Benchmark Evaluation

The evaluation code of TRACE is integrated into simple-romp, trace2/eval.py, trace2/evaluation folder.

To prepare the evaluation datasets, please refer to trace_dataset.md.

Please set the path to datasets in dataset_dir of simple-romp/trace2/eval.py, and then run:

DynaCam

For details of DynaCam dataset, please refer to [DynaCam Dataset], which directly provides predictions, and code for evaluation and visualization.

You may also evaluate on DynaCam via running

cd simple_romp/trace2
python -m eval --eval_dataset=DynaCam

MuPoTS

Please download the MuPoTS dataset from official website and the packed annotations from here. Then please set the mupots's dataset_dir in simple-romp/trace2/eval.py.

cd simple_romp/trace2
python -m eval --eval_dataset=mupots

Dyna3DPW

Please download the Dyna3DPW dataset from here. Then please set the Dyna3DPW's dataset_dir in simple-romp/trace2/eval.py.

cd simple_romp/trace2
python -m eval --eval_dataset=Dyna3DPW

3DPW

Due to an error here, we are sorry to report that the previous evaluation results on 3DPW were wrong. After correction, the results on 3DPW are PAMPJPE 50.8, MPJPE 80.3, PVE 98.1. We sincerely apologize for this error.

Please download the 3DPW dataset from official website and the packed annotations from here. Then please set the 3DPW's dataset_dir in simple-romp/trace2/eval.py.

cd simple_romp/trace2
python -m eval --eval_dataset=3DPW

Citation

@InProceedings{TRACE,
    author = {Sun, Yu and Bao, Qian and Liu, Wu and Mei, Tao and Black, Michael J.},
    title = {{TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments}}, 
    booktitle = {CVPR}, 
    year = {2023}}