EMA: Efficient Meshy Neural Fields
for Animatable Human Avatars

Xiaoke Huang Yiji Cheng Yansong Tang* Xiu Li Jie Zhou Jiwen Lu

Tsinghua University

Project Page Paper (arXiv) Paper (small) Video (Unlist)

Data

Download the data of ZJU-MoCap from NeuralBody and H36M from Animatable-NeRF (both projects are from ZJU).

We use the old version of the data to train and test our method. However, the new version of data provides better SMPL fitting which we think it could boost the performance of our method.

Store the data in data/, which are data/zju_mocap/ and data/h36m.

Data Preprocessing

Preprocessing data to "pose_data.pt"

Put SMPL neutral in data/

# ZJU-Mocap
# for i in 313 315 377 386 387 390 392 393 394; do
for i in 313 315 377 386; do
python scripts/convert_smpl_params_to_pose_data.py --smpl_dir data/zju_mocap/CoreView_${i}/new_params --base_dir data/zju_mocap/CoreView_${i}/ --smpl_model_path ./data/SMPL_NEUTRAL.pkl
done

# H36M
for i in 1 5 6 9 11; do 
echo $i
python scripts/convert_smpl_params_to_pose_data.py --smpl_dir data/h36m/S$i/Posing/new_params --base_dir data/h36m/S$i/Posing/ --smpl_model_path ./data/SMPL_NEUTRAL.pkl --interval 5
done

i=7
python scripts/convert_smpl_params_to_pose_data.py --smpl_dir data/h36m/S$i/Posing/new_params --base_dir data/h36m/S$i/Posing/ --smpl_model_path ./data/SMPL_NEUTRAL.pkl --interval 5 --num_frames 2916

i=8
python scripts/convert_smpl_params_to_pose_data.py --smpl_dir data/h36m/S$i/Posing/new_params --base_dir data/h36m/S$i/Posing/ --smpl_model_path ./data/SMPL_NEUTRAL.pkl --interval 5 --num_frames 1681

The template tets

Download the meshes Place them into tmp/quartet/meshes/.

Env

Build docker image.

cd docker
./make_image.sh ema:v1

Start the container:

docker run -itd \
--name ema-container \
--gpus all \
-v $(realpath .):$(realpath .) \
-v $(realpath ./data/zju_mocap/):$(realpath .)/data/zju_mocap \
-v $(realpath ./data/h36m/):$(realpath .)/data/h36m \
-w $(realpath .)  \
--network=host --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
ema:v1 bash

docker exec -it ema-container bash

Demo

Download the checkpoint into OUR_DIR

OUT_DIR=outputs/demo/
# On 171


WANDB_MODE=offline python visualize_inference.py \
--config-dir ${OUT_DIR}/hydra_config/ \
--config-name config \
external_ckpt_dir=${OUT_DIR}/ \
validate=true \
display=null \
out_dir=outputs/visualize \
no_train=true \
pre_load=false \
background=grey \
num_val_examples=null \
learn_non_rigid_offset=false \
validate_dataset.0.frame_interval=1 \
validate_dataset.0.begin_frame_id=0 \
validate_dataset.0.num_frames=1000 \
validate_dataset.0.order_camera_first=False \
validate_dataset.0.no_image_inputs=True \
validate_dataset.1.frame_interval=1 \
validate_dataset.1.begin_frame_id=0 \
validate_dataset.1.num_frames=1000 \
validate_dataset.1.order_camera_first=False \
validate_dataset.1.no_image_inputs=True \
num_workers_validate=10

Run Codes

Dev Run

Make sure everything is ok.

WANDB_MODE=offline \
python train_dyn.py exp_name=debug tet_dir=tmp/quartet/meshes/ lpips_loss_weight=1.0 iter=1 batch=1

We use wandb to minitor the performance. If you do not want to log with wandb, set WANDB_MODE=offline.

Training

Use meta_configs, e.g., meta_configs/base_config-1.h36m.yaml and meta_configs/base_config-1.zju_mocap.yaml:

Example codes:

CUDA_VISIBLE_DEVICES=0 python scripts/config_sweeper.py -s train_dyn.py -f $META_CONFIG -n \
    +train_dataset.texture_obj_path=data/toy_data_zju_format/SMPL/SMPL_female_default_resolution.obj +train_dataset.force_generate_synthesis_data=false train_dataset._target_=dataset.zju_mocap_synthesis.DatasetZJUMoCapSynthesis +validate_dataset.0.texture_obj_path='data/toy_data_zju_format/SMPL/SMPL_female_default_resolution.obj' validate_dataset.0._target_=dataset.zju_mocap_synthesis.DatasetZJUMoCapSynthesis +validate_dataset.0.force_generate_synthesis_data=false +validate_dataset.1.texture_obj_path='data/toy_data_zju_format/SMPL/SMPL_female_default_resolution.obj' validate_dataset.1._target_=dataset.zju_mocap_synthesis.DatasetZJUMoCapSynthesis +validate_dataset.1.force_generate_synthesis_data=false \
    +train_dataset.noise_scale=0.01 +train_dataset.add_noise=true

python scripts/config_sweeper.py -g='(5,)' -s train_dyn.py -f $META_CONFIG -n \
    +train_dataset.texture_obj_path=data/toy_data_zju_format/SMPL/SMPL_female_default_resolution.obj +train_dataset.force_generate_synthesis_data=false train_dataset._target_=dataset.zju_mocap_synthesis.DatasetZJUMoCapSynthesis +validate_dataset.0.texture_obj_path='data/toy_data_zju_format/SMPL/SMPL_female_default_resolution.obj' validate_dataset.0._target_=dataset.zju_mocap_synthesis.DatasetZJUMoCapSynthesis +validate_dataset.0.force_generate_synthesis_data=false +validate_dataset.1.texture_obj_path='data/toy_data_zju_format/SMPL/SMPL_female_default_resolution.obj' validate_dataset.1._target_=dataset.zju_mocap_synthesis.DatasetZJUMoCapSynthesis +validate_dataset.1.force_generate_synthesis_data=false \
    +train_dataset.noise_scale=0.2 +train_dataset.add_noise=true

Evaluation

may need to use +no_mesh_export. backward incompatible

OUT_DIR=/path/to/results


WANDB_MODE=offline \
CUDA_VISIBLE_DEVICES=0 \
python train_dyn.py --config-dir ${OUT_DIR}/.hydra/ --config-name config \
external_ckpt_dir=${OUT_DIR}/ckpts/ \
display=null \
validate=true \
compute_val_metrics=false \
save_val_images=false \
no_train=true \
no_mesh_export=true \
learn_non_rigid_offset=true \
num_workers_validate=10 \
out_dir='outputs/${exp_name}/eval/${subject_id}/${now:%y%m%d_%H%M%S}' \
+runtime_breakdown=True \
validate_dataset.0.frame_interval=1 \
validate_dataset.0.begin_frame_id=0 \
validate_dataset.0.num_frames=10 \
validate_dataset.0.order_camera_first=False \
validate_dataset.1.frame_interval=1 \
validate_dataset.1.begin_frame_id=0 \
validate_dataset.1.num_frames=10 \
validate_dataset.1.order_camera_first=False \
num_workers_validate=10 \
+validate_update_base_mesh=True \

++learning_rate_schedule_type=linear \
++learning_rate_schedule_steps=[] \
++mask_at_box_bound_pad=0.0 \
++use_vitruvian_pose=true \
subject_id=??? # for efficiency overrite subject_id
+dataset@_global_=???

Metrics

First install bc: apt-get install bc Then run metrics: bash scripts/compute_avarge_metric.sh

python scripts/metric_psnr_ssmi_lpips.py \
-t outputs/.../opt \
-g outputs/.../ref \
--exp_name metric \
--log_file tmp/metrics.log

Mesh Rendering Demo

OUT_DIR=
MESH_PATH=
MTL_PATH=null

WANDB_MODE=offline \
CUDA_VISIBLE_DEVICES=0 \
python demo_mesh_rendering.py --config-dir ${OUT_DIR}/.hydra/ --config-name config \
external_ckpt_dir=${OUT_DIR}/ckpts/ \
display=null \
validate=true \
compute_val_metrics=false \
save_val_images=true \
no_train=true \
no_mesh_export=true \
learn_non_rigid_offset=true \
out_dir='outputs/${exp_name}/mesh/${subject_id}/${now:%y%m%d_%H%M%S}' \
use_mesh=True \
learn_mesh_skinning=True \
learn_mesh_material_with_mlp=True \
++external_mtl_path=$MTL_PATH \
++use_tet_aabb_in_mesh=true \
++external_mesh_path=$MESH_PATH

OUT_DIR=path/to/results
MESH_PATH=tmp/230308.demo_mesh/mesh.uv_unwarp-manually.obj
MTL_PATH=tmp/230308.demo_mesh/sunflower.mtl

AIST

OUT_DIR=

WANDB_MODE=offline \
CUDA_VISIBLE_DEVICES=0 \
python demo_mesh_rendering.py --config-dir ${OUT_DIR}/.hydra/ --config-name config \
external_ckpt_dir=${OUT_DIR}/outlier_ckpts/ \
display=null \
validate=true \
compute_val_metrics=false \
save_val_images=true \
no_train=true \
no_mesh_export=true \
learn_non_rigid_offset=true \
out_dir='outputs/${exp_name}/aist/${subject_id}/${now:%y%m%d_%H%M%S}' \
use_mesh=False \
learn_mesh_skinning=True \
learn_mesh_material_with_mlp=True \
++external_mtl_path=null \
++use_tet_aabb_in_mesh=true \
++use_vitruvian_pose=True \
+dataset@_global_=demo/aist-h36m.yaml


OUT_DIR=path/to/results


# aist gBR_sBM_cAll_d04_mBR1_ch10
# 01 left right cross hand
# 02 left right cross legs
# 03 swimming
# 04 left right
# 05 left right and touch ground
# 06 left right cross hand
# 07 slip on the ground
# 08 slip on the ground
# 09 

+dataset@_global_=demo/aist-h36m.yaml aist_pose_pkl_path=data/aist/motions/
gBR_sBM_cAll_d04_mBR0_ch04
gWA_sBM_cAll_d25_mWA1_ch02
gLH_sFM_cAll_d16_mLH5_ch06
gLO_sBM_cAll_d14_mLO5_ch02
gMH_sBM_cAll_d24_mMH2_ch08
gWA_sFM_cAll_d26_mWA3_ch11

Citation

If you find this repository useful, please consider giving a star ⭐ and citation 🦖:

@article{Huang2023EMA,
    title={Efficient Meshy Neural Fields for Animatable Human Avatars},
    author={Xiaoke Huang and Yiji Cheng and Yansong Tang and Xiu Li and Jie Zhou and Jiwen Lu},
    journal={arXiv},
    year={2023},
    volume={abs/2303.12965}
}

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EMA: Efficient Meshy Neural Fields
for Animatable Human Avatars

Data

Data Preprocessing

The template tets

Env

Demo

Run Codes

Dev Run

Training

Evaluation

Metrics

Mesh Rendering Demo

Citation

About

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
configs		configs
dataset		dataset
docker		docker
geometry		geometry
meta_configs		meta_configs
render		render
scripts		scripts
utils		utils
.gitignore		.gitignore
README.md		README.md
demo_mesh_rendering.py		demo_mesh_rendering.py
train_dyn.py		train_dyn.py
visualize_inference.py		visualize_inference.py

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EMA: Efficient Meshy Neural Fields for Animatable Human Avatars

Data

Data Preprocessing

The template tets

Env

Demo

Run Codes

Dev Run

Training

Evaluation

Metrics

Mesh Rendering Demo

Citation

About

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EMA: Efficient Meshy Neural Fields
for Animatable Human Avatars

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