src_joints2smpl_demo/mesh/smpl.py

# This code is based on https://github.com/Mathux/ACTOR.git
import contextlib

import numpy as np
import torch
from smplx import SMPLLayer as _SMPLLayer
from smplx.lbs import vertices2joints

# action2motion_joints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21, 24, 38]
# change 0 and 8
action2motion_joints = [8, 1, 2, 3, 4, 5, 6, 7, 0, 9, 10, 11, 12, 13, 14, 21, 24, 38]

# from utils.config import JOINT_REGRESSOR_TRAIN_EXTRA, SMPL_MODEL_PATH
import os

SMPL_DATA_PATH = "./body_models/smpl"

# SMPL_KINTREE_PATH = os.path.join(SMPL_DATA_PATH, "kintree_table.pkl")
SMPL_MODEL_PATH = os.path.join(SMPL_DATA_PATH, "SMPL_NEUTRAL.pkl")
JOINT_REGRESSOR_TRAIN_EXTRA = os.path.join(SMPL_DATA_PATH, "J_regressor_extra.npy")

# ROT_CONVENTION_TO_ROT_NUMBER = {
#     'legacy': 23,
#     'no_hands': 21,
#     'full_hands': 51,
#     'mitten_hands': 33,
# }

# GENDERS = ['neutral', 'male', 'female']
# NUM_BETAS = 10

JOINTSTYPE_ROOT = {
    "a2m": 0,  # action2motion
    "smpl": 0,
    "a2mpl": 0,  # set(smpl, a2m)
    "vibe": 8,
}  # 0 is the 8 position: OP MidHip below

JOINT_MAP = {
    "OP Nose": 24,
    "OP Neck": 12,
    "OP RShoulder": 17,
    "OP RElbow": 19,
    "OP RWrist": 21,
    "OP LShoulder": 16,
    "OP LElbow": 18,
    "OP LWrist": 20,
    "OP MidHip": 0,
    "OP RHip": 2,
    "OP RKnee": 5,
    "OP RAnkle": 8,
    "OP LHip": 1,
    "OP LKnee": 4,
    "OP LAnkle": 7,
    "OP REye": 25,
    "OP LEye": 26,
    "OP REar": 27,
    "OP LEar": 28,
    "OP LBigToe": 29,
    "OP LSmallToe": 30,
    "OP LHeel": 31,
    "OP RBigToe": 32,
    "OP RSmallToe": 33,
    "OP RHeel": 34,
    "Right Ankle": 8,
    "Right Knee": 5,
    "Right Hip": 45,
    "Left Hip": 46,
    "Left Knee": 4,
    "Left Ankle": 7,
    "Right Wrist": 21,
    "Right Elbow": 19,
    "Right Shoulder": 17,
    "Left Shoulder": 16,
    "Left Elbow": 18,
    "Left Wrist": 20,
    "Neck (LSP)": 47,
    "Top of Head (LSP)": 48,
    "Pelvis (MPII)": 49,
    "Thorax (MPII)": 50,
    "Spine (H36M)": 51,
    "Jaw (H36M)": 52,
    "Head (H36M)": 53,
    "Nose": 24,
    "Left Eye": 26,
    "Right Eye": 25,
    "Left Ear": 28,
    "Right Ear": 27,
}

JOINT_NAMES = [
    "OP Nose",
    "OP Neck",
    "OP RShoulder",
    "OP RElbow",
    "OP RWrist",
    "OP LShoulder",
    "OP LElbow",
    "OP LWrist",
    "OP MidHip",
    "OP RHip",
    "OP RKnee",
    "OP RAnkle",
    "OP LHip",
    "OP LKnee",
    "OP LAnkle",
    "OP REye",
    "OP LEye",
    "OP REar",
    "OP LEar",
    "OP LBigToe",
    "OP LSmallToe",
    "OP LHeel",
    "OP RBigToe",
    "OP RSmallToe",
    "OP RHeel",
    "Right Ankle",
    "Right Knee",
    "Right Hip",
    "Left Hip",
    "Left Knee",
    "Left Ankle",
    "Right Wrist",
    "Right Elbow",
    "Right Shoulder",
    "Left Shoulder",
    "Left Elbow",
    "Left Wrist",
    "Neck (LSP)",
    "Top of Head (LSP)",
    "Pelvis (MPII)",
    "Thorax (MPII)",
    "Spine (H36M)",
    "Jaw (H36M)",
    "Head (H36M)",
    "Nose",
    "Left Eye",
    "Right Eye",
    "Left Ear",
    "Right Ear",
]


# adapted from VIBE/SPIN to output smpl_joints, vibe joints and action2motion joints
class SMPL(_SMPLLayer):
    """Extension of the official SMPL implementation to support more joints"""

    def __init__(self, model_path=SMPL_MODEL_PATH, **kwargs):
        kwargs["model_path"] = model_path

        # remove the verbosity for the 10-shapes beta parameters
        with contextlib.redirect_stdout(None):
            super(SMPL, self).__init__(**kwargs)

        J_regressor_extra = np.load(JOINT_REGRESSOR_TRAIN_EXTRA)
        self.register_buffer(
            "J_regressor_extra", torch.tensor(J_regressor_extra, dtype=torch.float32)
        )
        vibe_indexes = np.array([JOINT_MAP[i] for i in JOINT_NAMES])
        a2m_indexes = vibe_indexes[action2motion_joints]
        smpl_indexes = np.arange(24)
        a2mpl_indexes = np.unique(np.r_[smpl_indexes, a2m_indexes])

        self.maps = {
            "vibe": vibe_indexes,
            "a2m": a2m_indexes,
            "smpl": smpl_indexes,
            "a2mpl": a2mpl_indexes,
        }

    def forward(self, *args, **kwargs):
        smpl_output = super(SMPL, self).forward(*args, **kwargs)

        extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices)
        all_joints = torch.cat([smpl_output.joints, extra_joints], dim=1)

        output = {"vertices": smpl_output.vertices}

        for joinstype, indexes in self.maps.items():
            output[joinstype] = all_joints[:, indexes]

        return output