easyHumanNeRF

End-to-end Implementation of HumanNeRF with custom dataset

paper HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video

Demo of custom data

FreeView	Movement	TPose

Pipeline

Prerequisite

`Configure environment`

Create and activate a virtual environment.

conda create --name humannerf python=3.7
conda activate humannerf

Install the required packages.

pip install -r requirements.txt

`Download SMPL model`

Download the gender neutral SMPL model from here, and unpack mpips_smplify_public_v2.zip.

Copy the smpl model.

SMPL_DIR=/path/to/smpl
MODEL_DIR=$SMPL_DIR/smplify_public/code/models
cp $MODEL_DIR/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl third_parties/smpl/models

Follow this page to remove Chumpy objects from the SMPL model.

Fast Train

git clone https://github.com/IVL-PKU/easyHumanNeRF.git
cd easyHumanNeRF/

put your images under the folder ./workspace/demo/

python e2e_train.py --workspace ./workspace/demo/

`Render output`

Render the frame input (i.e., observed motion sequence).

python run.py \
    --type movement \
    --cfg configs/human_nerf/wild/monocular/adventure.yaml

Run free-viewpoint rendering on a particular frame (e.g., frame 128).

python run.py \
    --type freeview \
    --cfg configs/human_nerf/wild/monocular/adventure.yaml \
    freeview.frame_idx 128

Render the learned canonical appearance (T-pose).

python run.py \
    --type tpose \
    --cfg configs/human_nerf/wild/monocular/adventure.yaml

In addition, you can find the rendering scripts in scripts/wild.

TODO LIST

Acknowledge

easyHumanNeRF is an integration of HumanNeRF, VIBE and YOLOv7. If this is helpful to you, please give stars to the above works. Thanks!

IVL-PKU/easyHumanNeRF

easyHumanNeRF

Demo of custom data

FreeView

Movement

TPose