Awesome

📑 Abstract

📍 InstructHumans Results

Left to right: Original, "Turn the person into a clown", "Turn the person into Lord Voldemort", "Put the person in a suit", "A bronze statue".

📦 Installation

Our code has been tested with PyTorch 2.0.1, CUDA 11.7. But other versions should also be fine.

• Clone this repo and create conda environment:

  git clone https://github.com/viridityzhu/InstructHumans.git
  cd InstructHumans
  conda env create -f environment.yml
  conda activate InstructHumans
  

• kaolin requires to be installed separately. Check their docs - Installation. They provided prebuilt wheels for some older versions of CUDA and pytorch.

  TORCH_VER="2.0.1"
  CUDA_VER="117"
  pip install kaolin==0.14.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-$TORCH_VER\_cu$CUDA_VER\.html
  

  <details> <summary>Click me for issues with kaolin installation</summary>

  If you encounter error when importing kaolin: from kaolin import _C ImportError, it may due to incompatibility with your CUDA version.

  Note we use cuda version 11.7. Try install the specific version in the conda environment:

  conda install -c conda-forge cudatoolkit=11.7
  

  Alternatively, you can install the compatible versions all together:

  conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia
  

  Then, reinstall kaolin with --force option.

  </details>

📂 Data preparation

1. Download the CustomHumans dataset here. We use their provided pretrained checkpoint. Put it into the checkpoints folder in the following structure:

checkpoints/demo/model-1000.pth

2. Download SMPL-X models and move them to the smplx folder in the following structure:

    smplx
    ├── SMPLX_NEUTRAL.pkl
    ├── SMPLX_NEUTRAL.npz
    ├── SMPLX_MALE.pkl
    ├── SMPLX_MALE.npz
    ├── SMPLX_FEMALE.pkl
    └── SMPLX_FEMALE.npz

🎲 3D Human Editing

Checkpoints

In google drive link, we provide our pre-trained checkpoints, pre-processed data, and an example animation motion.

Pre-process

This pre-process helps speed up the editing. In the previous link, we provide a prepared data file for human id 32 for a quick start.

The data file can be specified by --traced_points_data_root prepared_tracing.h5 when editing.

To prepare the data for other human IDs, use the following:

python -m tools.prepare_tracing --subject_list [9,15]

--subject_list [] specifies subject ids to be processed. Processing one subject takes around 30 minutes. (This will pre-sample intermediate results of ray tracing for each human subject and cache them in a h5 file. This way, we can avoid repeating ray tracing and extremely speed up the editing procedure.)

Edit human with instructions

Run the below command, and you will edit the sample human (id 32 in the dataset) into a clown:

python edit.py --instruction "Turn him into a clown" --id 32

Here are some configuration flags you can use; otherwise you can find full default settings in config.yaml and descriptions in lib/utils/config.py:

• --instruction: textual editing instruction.
• --id: human subject index. Use this to indicate the original human to be edited. They should be included in the pretrained checkpoints of CustomHumans.
• --save-path: path to the folder to save the checkpoints.
• --config: path to the config file. The default is config.yaml
• --wandb: we use wandb for monitoring the training. Activate this flag if you want to use it.
• --caption_ori and --caption_tgt: these do not affect the editing, but help calculate evaluation metrics. They are captions describing the original or target images.
• --sampl_strategy: to select SDS-E / SDS-E' / SDS to use, set "dt+ds" / "dt+ds2" / "ori", respectively. More details can be found in our paper.

Test a trained checkpoint

• We provide test/test_cp.py to test a trained checkpoint. Usage:

python -m test.test_cp \
    --edit_checkpoint_file "checkpoints/test/elf32.pth.tar" \
    --instruction "Turn him into Tolkien Elf" \
    --id 32 \
    --caption_ori "A photo of a person" --caption_tgt "A photo of a Tolkien Elf"
    --cal-metric 1 \
    --render-more 0

• --cal-metric: whether to calculate the evaluation metrics. If wandb is disabled, the metrics are printed; otherwise, they are uploaded to wandb.
• --render-more: by default, for both the original and edited humans, we render 15 images for visualization, and they are saved in the same directory as the checkpoint file. You can set this to 1 if you want to render more images.
• The other supported arguments are the same as edit.py.

▶️ Demo for animating an edited human

1. Prepare SMPL-X models with desired poses. For example, you can download the MotionX dataset, and use tools/load_motionx_smplx.py to convert its SMPL-X JSON data into .obj files. We've provided an example motion clip in the checkpoints link.

   Example usage:

   python tools/load_motionx_smplx.py -i test/motionX_example
   

2. Reposing and rendering, usage:

   python -m test.drive_motion \
       --id 9 # subject id (only affect the geometry) \
       --load_edit_checkpoint True \
       --edit_checkpoint_file checkpoints/joker9/checkpoint_step1000.pth.tar  # texture checkpoint \
       --motion-folder test/motionX_example/Electrician_dancing # many obj files defining the motion, prepared in step 1 \
       --output-name joker9 # output folder's name \
       --n_views 4 # rendered views per frame \
   

   Once done, you'll get generated rendered per frame images as well as an mp4 video in test/outputs/.

📄 Citation

If you found this repository/our paper useful, please consider citing:

@article{zhu2024InstructHumans,
         author={Zhu, Jiayin and Yang, Linlin and Yao, Angela},
         title={InstructHumans: Editing Animated 3D Human Textures with Instructions},
         journal={arXiv preprint arXiv:2404.04037},
         year={2024}
} 

👏 Acknowledgement

We sincerely thank the authors for their awesome works in editable-humans and instruct-nerf2nerf!