# Copyright 2023, Junjia LIU, jjliu@mae.cuhk.edu.hk
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Attention: Since the Autodesk FBX SDK just supports Python 3.7, this script should be run with Python 3.7.
"""
import multiprocessing
import os.path
import click
import glob
import numpy as np
import rofunc as rf
from rofunc.utils.datalab.poselib.poselib.core.rotation3d import *
from rofunc.utils.datalab.poselib.poselib.skeleton.skeleton3d import (
SkeletonState,
SkeletonMotion,
)
from rofunc.utils.datalab.poselib.poselib.visualization.common import (
plot_skeleton_motion_interactive,
plot_skeleton_state,
)
[docs]def motion_from_fbx(fbx_file_path, root_joint, fps=60, visualize=True):
# import fbx file - make sure to provide a valid joint name for root_joint
motion = SkeletonMotion.from_fbx(
fbx_file_path=fbx_file_path, root_joint=root_joint, fps=fps
)
# visualize motion
if visualize:
plot_skeleton_motion_interactive(motion)
return motion
[docs]def motion_retargeting(retarget_cfg, source_motion, visualize=False):
# load and visualize t-pose files
source_tpose = SkeletonState.from_file(retarget_cfg["source_tpose"])
if visualize:
plot_skeleton_state(source_tpose, "source_tpose")
target_tpose = SkeletonState.from_file(retarget_cfg["target_tpose"])
if visualize:
plot_skeleton_state(target_tpose, "target_tpose")
# parse data from retarget config
rotation_to_target_skeleton = torch.tensor(retarget_cfg["rotation"])
# run retargeting
target_motion = source_motion.retarget_to_by_tpose(
joint_mapping=retarget_cfg["joint_mapping"],
source_tpose=source_tpose,
target_tpose=target_tpose,
rotation_to_target_skeleton=rotation_to_target_skeleton,
scale_to_target_skeleton=retarget_cfg["scale"],
)
# plot_skeleton_motion_interactive(target_motion)
# keep frames between [trim_frame_beg, trim_frame_end - 1]
frame_beg = retarget_cfg["trim_frame_beg"]
frame_end = retarget_cfg["trim_frame_end"]
if frame_beg == -1:
frame_beg = 0
if frame_end == -1:
frame_end = target_motion.local_rotation.shape[0]
local_rotation = target_motion.local_rotation
root_translation = target_motion.root_translation
local_rotation = local_rotation[frame_beg:frame_end, ...]
root_translation = root_translation[frame_beg:frame_end, ...]
new_sk_state = SkeletonState.from_rotation_and_root_translation(
target_motion.skeleton_tree, local_rotation, root_translation, is_local=True
)
target_motion = SkeletonMotion.from_skeleton_state(
new_sk_state, fps=target_motion.fps
)
# need to convert some joints from 3D to 1D (e.g. elbows and knees)
# target_motion = _project_joints(target_motion)
# move the root so that the feet are on the ground
local_rotation = target_motion.local_rotation
root_translation = target_motion.root_translation
tar_global_pos = target_motion.global_translation
min_h = torch.min(tar_global_pos[..., 2])
root_translation[:, 2] += -min_h
# adjust the height of the root to avoid ground penetration
root_height_offset = retarget_cfg["root_height_offset"]
root_translation[:, 2] += root_height_offset
new_sk_state = SkeletonState.from_rotation_and_root_translation(
target_motion.skeleton_tree, local_rotation, root_translation, is_local=True
)
target_motion = SkeletonMotion.from_skeleton_state(
new_sk_state, fps=target_motion.fps
)
# save retargeted motion
target_motion.to_file(retarget_cfg["target_motion_path"])
if visualize:
# visualize retargeted motion
plot_skeleton_motion_interactive(target_motion)
[docs]def amp_npy_from_fbx(fbx_file, tpose_file, amp_tpose_file, verbose=True):
"""
This scripts shows how to retarget a motion clip from the source skeleton to a target skeleton.
Data required for retargeting are stored in a retarget config dictionary as a json file. This file contains:
- source_motion: a SkeletonMotion npy format representation of a motion sequence. The motion clip should use the
same skeleton as the source T-Pose skeleton.
- target_motion_path: path to save the retargeted motion to
- source_tpose: a SkeletonState npy format representation of the source skeleton in it's T-Pose state
- target_tpose: a SkeletonState npy format representation of the target skeleton in it's T-Pose state
(pose should match source T-Pose)
- joint_mapping: mapping of joint names from source to target
- rotation: root rotation offset from source to target skeleton (for transforming across different orientation
axes), represented as a quaternion in XYZW order.
- scale: scale offset from source to target skeleton
"""
target_motion_path = fbx_file[:-4] + ".npy"
config = {
"target_motion_path": target_motion_path,
"source_tpose": tpose_file,
"target_tpose": amp_tpose_file,
"joint_mapping": {
"Hips": "pelvis",
"LeftUpLeg": "left_thigh",
"LeftLeg": "left_shin",
"LeftToeBase": "left_foot",
"RightUpLeg": "right_thigh",
"RightLeg": "right_shin",
"RightToeBase": "right_foot",
"Spine": "torso",
"Head": "head",
"LeftShoulder": "left_shoulder",
"LeftArm": "left_upper_arm",
"LeftForeArm": "left_lower_arm",
"LeftHand": "left_hand",
"RightShoulder": "right_shoulder",
"RightArm": "right_upper_arm",
"RightForeArm": "right_lower_arm",
"RightHand": "right_hand",
},
# "rotation": [0.707, 0, 0, 0.707], xyzw
"rotation": [0.5, 0.5, 0.5, 0.5],
"scale": 0.001,
"root_height_offset": 0.0,
"trim_frame_beg": 0,
"trim_frame_end": -1,
}
motion = motion_from_fbx(fbx_file, root_joint="Hips", fps=60, visualize=verbose)
config["target_motion_path"] = fbx_file.replace(".fbx", "_amp.npy")
motion_retargeting(config, motion, visualize=verbose)
[docs]def main(is_parallel, verbose):
data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../../../../examples/data/hotu")
fbx_files = sorted(glob.glob(os.path.join(data_dir, '*.fbx')))
tpose_file = os.path.join(data_dir, "xsens_fbx_tpose.npy")
amp_humanoid_tpose_file = os.path.join(os.path.join(
os.path.dirname(os.path.abspath(__file__)), "../data/hotu_humanoid_generated_tpose.npy"
))
if is_parallel:
pool = multiprocessing.Pool()
pool.map(amp_npy_from_fbx, fbx_files)
else:
for fbx_file in fbx_files:
amp_npy_from_fbx(fbx_file, tpose_file, amp_humanoid_tpose_file, verbose)
if __name__ == "__main__":
main(False, False)
