233 lines
10 KiB
Bash
233 lines
10 KiB
Bash
export GPU="0"
|
|
export CAMERA="PINHOLE"
|
|
export EXP_NAME_1="stage1"
|
|
export EXP_NAME_2="stage2"
|
|
export EXP_NAME_3="stage3"
|
|
|
|
#
|
|
# Ensure that the following environment variables are accessible to the script:
|
|
# PROJECT_DIR and DATA_PATH
|
|
#
|
|
|
|
# Need to use this to activate conda environments
|
|
eval "$(conda shell.bash hook)"
|
|
|
|
#################
|
|
# PREPROCESSING #
|
|
#################
|
|
|
|
# Arrange raw images into a 3D Gaussian Splatting format
|
|
conda deactivate && conda activate gaussian_splatting_hair
|
|
cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python preprocess_raw_images.py \
|
|
--data_path $DATA_PATH
|
|
|
|
# Run COLMAP reconstruction and undistort the images and cameras
|
|
conda deactivate && conda activate gaussian_splatting_hair
|
|
cd $PROJECT_DIR/src
|
|
CUDA_VISIBLE_DEVICES="$GPU" python convert.py -s $DATA_PATH \
|
|
--camera $CAMERA --max_size 1024
|
|
|
|
# Run Matte-Anything
|
|
conda deactivate && conda activate matte_anything
|
|
cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python calc_masks.py \
|
|
--data_path $DATA_PATH --image_format png --max_size 2048
|
|
|
|
# Filter images using their IQA scores
|
|
conda deactivate && conda activate gaussian_splatting_hair
|
|
cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python filter_extra_images.py \
|
|
--data_path $DATA_PATH --max_imgs 128
|
|
|
|
# Resize images
|
|
conda deactivate && conda activate gaussian_splatting_hair
|
|
cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python resize_images.py --data_path $DATA_PATH
|
|
|
|
# Calculate orientation maps
|
|
conda deactivate && conda activate gaussian_splatting_hair
|
|
cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python calc_orientation_maps.py \
|
|
--img_path $DATA_PATH/images_2 \
|
|
--mask_path $DATA_PATH/masks_2/hair \
|
|
--orient_dir $DATA_PATH/orientations_2/angles \
|
|
--conf_dir $DATA_PATH/orientations_2/vars \
|
|
--filtered_img_dir $DATA_PATH/orientations_2/filtered_imgs \
|
|
--vis_img_dir $DATA_PATH/orientations_2/vis_imgs
|
|
|
|
# Run OpenPose
|
|
conda deactivate && cd $PROJECT_DIR/ext/openpose
|
|
mkdir $DATA_PATH/openpose
|
|
CUDA_VISIBLE_DEVICES="$GPU" ./build/examples/openpose/openpose.bin \
|
|
--image_dir $DATA_PATH/images_4 \
|
|
--scale_number 4 --scale_gap 0.25 --face --hand --display 0 \
|
|
--write_json $DATA_PATH/openpose/json \
|
|
--write_images $DATA_PATH/openpose/images --write_images_format jpg
|
|
|
|
# Run Face-Alignment
|
|
conda deactivate && conda activate gaussian_splatting_hair
|
|
cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python calc_face_alignment.py \
|
|
--data_path $DATA_PATH --image_dir "images_4"
|
|
|
|
# Run PIXIE
|
|
conda deactivate && conda activate pixie-env
|
|
cd $PROJECT_DIR/ext/PIXIE
|
|
CUDA_VISIBLE_DEVICES="$GPU" python demos/demo_fit_face.py \
|
|
-i $DATA_PATH/images_4 -s $DATA_PATH/pixie \
|
|
--saveParam True --lightTex False --useTex False \
|
|
--rasterizer_type pytorch3d
|
|
|
|
# Merge all PIXIE predictions in a single file
|
|
conda deactivate && conda activate gaussian_splatting_hair
|
|
cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python merge_smplx_predictions.py \
|
|
--data_path $DATA_PATH
|
|
|
|
# Convert COLMAP cameras to txt
|
|
conda deactivate && conda activate gaussian_splatting_hair
|
|
mkdir $DATA_PATH/sparse_txt
|
|
CUDA_VISIBLE_DEVICES="$GPU" colmap model_converter \
|
|
--input_path $DATA_PATH/sparse/0 \
|
|
--output_path $DATA_PATH/sparse_txt --output_type TXT
|
|
|
|
# Convert COLMAP cameras to H3DS format
|
|
conda deactivate && conda activate gaussian_splatting_hair
|
|
cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python colmap_parsing.py \
|
|
--path_to_scene $DATA_PATH
|
|
|
|
# Remove raw files to preserve disk space
|
|
rm -rf $DATA_PATH/input $DATA_PATH/images $DATA_PATH/masks $DATA_PATH/iqa*
|
|
|
|
##################
|
|
# RECONSTRUCTION #
|
|
##################
|
|
|
|
export EXP_PATH_1=$DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1
|
|
|
|
# Run 3D Gaussian Splatting reconstruction
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src
|
|
CUDA_VISIBLE_DEVICES="$GPU" python train_gaussians.py \
|
|
-s $DATA_PATH -m "$EXP_PATH_1" -r 1 --port "888$GPU" \
|
|
--trainable_cameras --trainable_intrinsics --use_barf \
|
|
--lambda_dorient 0.1
|
|
|
|
# Run FLAME mesh fitting
|
|
conda activate gaussian_splatting_hair
|
|
cd $PROJECT_DIR/ext/NeuralHaircut/src/multiview_optimization
|
|
|
|
CUDA_VISIBLE_DEVICES="$GPU" python fit.py --conf confs/train_person_1.conf \
|
|
--batch_size 1 --train_rotation True --fixed_images True \
|
|
--save_path $DATA_PATH/flame_fitting/$EXP_NAME_1/stage_1 \
|
|
--data_path $DATA_PATH \
|
|
--fitted_camera_path $EXP_PATH_1/cameras/30000_matrices.pkl
|
|
|
|
CUDA_VISIBLE_DEVICES="$GPU" python fit.py --conf confs/train_person_1.conf \
|
|
--batch_size 4 --train_rotation True --fixed_images True \
|
|
--save_path $DATA_PATH/flame_fitting/$EXP_NAME_1/stage_2 \
|
|
--checkpoint_path $DATA_PATH/flame_fitting/$EXP_NAME_1/stage_1/opt_params_final \
|
|
--data_path $DATA_PATH \
|
|
--fitted_camera_path $EXP_PATH_1/cameras/30000_matrices.pkl
|
|
|
|
CUDA_VISIBLE_DEVICES="$GPU" python fit.py --conf confs/train_person_1_.conf \
|
|
--batch_size 32 --train_rotation True --train_shape True \
|
|
--save_path $DATA_PATH/flame_fitting/$EXP_NAME_1/stage_3 \
|
|
--checkpoint_path $DATA_PATH/flame_fitting/$EXP_NAME_1/stage_2/opt_params_final \
|
|
--data_path $DATA_PATH \
|
|
--fitted_camera_path $EXP_PATH_1/cameras/30000_matrices.pkl
|
|
|
|
# Crop the reconstructed scene
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python scale_scene_into_sphere.py \
|
|
--path_to_data $DATA_PATH \
|
|
-m "$DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1" --iter 30000
|
|
|
|
# Remove hair Gaussians that intersect with the FLAME head mesh
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python filter_flame_intersections.py \
|
|
--flame_mesh_dir $DATA_PATH/flame_fitting/$EXP_NAME_1 \
|
|
-m "$DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1" --iter 30000 \
|
|
--project_dir $PROJECT_DIR/ext/NeuralHaircut
|
|
|
|
# Run rendering for training views
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src
|
|
CUDA_VISIBLE_DEVICES="$GPU" python render_gaussians.py \
|
|
-s $DATA_PATH -m "$DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1" \
|
|
--skip_test --scene_suffix "_cropped" --iteration 30000 \
|
|
--trainable_cameras --trainable_intrinsics --use_barf
|
|
|
|
# Get FLAME mesh scalp maps
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python extract_non_visible_head_scalp.py \
|
|
--project_dir $PROJECT_DIR/ext/NeuralHaircut --data_dir $DATA_PATH \
|
|
--flame_mesh_dir $DATA_PATH/flame_fitting/$EXP_NAME_1 \
|
|
--cams_path $DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1/cameras/30000_matrices.pkl \
|
|
-m "$DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1"
|
|
|
|
# Run latent hair strands reconstruction
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src
|
|
CUDA_VISIBLE_DEVICES="$GPU" python train_latent_strands.py \
|
|
-s $DATA_PATH -m "$DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1" -r 1 \
|
|
--model_path_hair "$DATA_PATH/strands_reconstruction/$EXP_NAME_2" \
|
|
--flame_mesh_dir "$DATA_PATH/flame_fitting/$EXP_NAME_1" \
|
|
--pointcloud_path_head "$EXP_PATH_1/point_cloud_filtered/iteration_30000/raw_point_cloud.ply" \
|
|
--hair_conf_path "$PROJECT_DIR/src/arguments/hair_strands_textured.yaml" \
|
|
--lambda_dmask 0.1 --lambda_dorient 0.1 --lambda_dsds 0.01 \
|
|
--load_synthetic_rgba --load_synthetic_geom --binarize_masks --iteration_data 30000 \
|
|
--trainable_cameras --trainable_intrinsics --use_barf \
|
|
--iterations 20000 --port "800$GPU"
|
|
|
|
# Run hair strands reconstruction
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src
|
|
CUDA_VISIBLE_DEVICES="$GPU" python train_strands.py \
|
|
-s $DATA_PATH -m "$DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1" -r 1 \
|
|
--model_path_curves "$DATA_PATH/curves_reconstruction/$EXP_NAME_3" \
|
|
--flame_mesh_dir "$DATA_PATH/flame_fitting/$EXP_NAME_1" \
|
|
--pointcloud_path_head "$EXP_PATH_1/point_cloud_filtered/iteration_30000/raw_point_cloud.ply" \
|
|
--start_checkpoint_hair "$DATA_PATH/strands_reconstruction/$EXP_NAME_2/checkpoints/20000.pth" \
|
|
--hair_conf_path "$PROJECT_DIR/src/arguments/hair_strands_textured.yaml" \
|
|
--lambda_dmask 0.1 --lambda_dorient 0.1 --lambda_dsds 0.01 \
|
|
--load_synthetic_rgba --load_synthetic_geom --binarize_masks --iteration_data 30000 \
|
|
--position_lr_init 0.0000016 --position_lr_max_steps 10000 \
|
|
--trainable_cameras --trainable_intrinsics --use_barf \
|
|
--iterations 10000 --port "800$GPU"
|
|
|
|
rm -rf "$DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1/train_cropped"
|
|
|
|
##################
|
|
# VISUALIZATIONS #
|
|
##################
|
|
|
|
# Export the resulting strands as pkl and ply
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src/preprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python export_curves.py \
|
|
--data_dir $DATA_PATH --model_name $EXP_NAME_3 --iter 10000 \
|
|
--flame_mesh_path "$DATA_PATH/flame_fitting/$EXP_NAME_1/stage_3/mesh_final.obj" \
|
|
--scalp_mesh_path "$DATA_PATH/flame_fitting/$EXP_NAME_1/scalp_data/scalp.obj" \
|
|
--hair_conf_path "$PROJECT_DIR/src/arguments/hair_strands_textured.yaml"
|
|
|
|
# Render the visualizations
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src/postprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python render_video.py \
|
|
--blender_path "$BLENDER_DIR" --input_path "$DATA_PATH" \
|
|
--exp_name_1 "$EXP_NAME_1" --exp_name_3 "$EXP_NAME_3"
|
|
|
|
# Render the strands
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src
|
|
CUDA_VISIBLE_DEVICES="$GPU" python render_strands.py \
|
|
-s $DATA_PATH --data_dir "$DATA_PATH" --data_device 'cpu' --skip_test \
|
|
-m "$DATA_PATH/3d_gaussian_splatting/$EXP_NAME_1" --iteration 30000 \
|
|
--flame_mesh_dir "$DATA_PATH/flame_fitting/$EXP_NAME_1" \
|
|
--model_hair_path "$DATA_PATH/curves_reconstruction/$EXP_NAME_3" \
|
|
--hair_conf_path "$PROJECT_DIR/src/arguments/hair_strands_textured.yaml" \
|
|
--checkpoint_hair "$DATA_PATH/strands_reconstruction/$EXP_NAME_2/checkpoints/20000.pth" \
|
|
--checkpoint_curves "$DATA_PATH/curves_reconstruction/$EXP_NAME_3/checkpoints/10000.pth" \
|
|
--pointcloud_path_head "$EXP_PATH_1/point_cloud/iteration_30000/raw_point_cloud.ply" \
|
|
--interpolate_cameras
|
|
|
|
# Make the video
|
|
conda activate gaussian_splatting_hair && cd $PROJECT_DIR/src/postprocessing
|
|
CUDA_VISIBLE_DEVICES="$GPU" python concat_video.py \
|
|
--input_path "$DATA_PATH" --exp_name_3 "$EXP_NAME_3" |