commit f14e9ef3a13e306ad909a682554a997d9f5f11cc Author: gulvarol Date: Wed May 22 18:03:53 2019 +0200 Init. diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c50674d --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +*.sw* +*.bak +*_bak.py + +.cache/ +__pycache__/ +build/ +dist/ + +smpl/native/models/*.pkl diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..dcd5db1 --- /dev/null +++ b/README.md @@ -0,0 +1,54 @@ +SMPL layer for PyTorch +======= + +[SMPL](http://smpl.is.tue.mpg.de) human body [\[1\]](#references) layer for [PyTorch](https://pytorch.org/) (tested with v0.4 and v1.x) +is a differentiable PyTorch layer that deterministically maps from pose and shape parameters to human body joints and vertices. +It can be integrated into any architecture as a differentiable layer to predict body meshes. +The code is adapted from the [manopth](https://github.com/hassony2/manopth) repository by [Yana Hasson](https://github.com/hassony2). + +

+smpl +

+ +## Setting up + +* Dependencies: + * Install the dependencies listed in [environment.yml](environment.yml) + * In an existing conda environment, `conda env update -f environment.yml` + * In a new environment, `conda env create -f environment.yml`, will create a conda environment named `smplpytorch` +* Download SMPL pickle files: + * Download the models from the [SMPL website](http://smpl.is.tue.mpg.de/) by choosing "SMPL for Python users". Note that you need to comply with the [SMPL model license](http://smpl.is.tue.mpg.de/license_model). + * Extract and copy the `models` folder into the `smpl/native/` folder. + +## Demo + +Forward pass the randomly created pose and shape parameters from the SMPL layer and display the human body mesh and joints: + +`python demo.py` + +## Acknowledgements +The code **largely** builds on the [manopth](https://github.com/hassony2/manopth) repository from [Yana Hasson](https://github.com/hassony2), which implements the [MANO](http://mano.is.tue.mpg.de) hand model [\[2\]](#references) layer. + +The code is a PyTorch port of the original [SMPL](http://smpl.is.tue.mpg.de) model from [chumpy](https://github.com/mattloper/chumpy). It builds on the work of [Loper](https://github.com/mattloper) et al. [\[1\]](#references). + +The code [reuses](https://github.com/gulvarol/smpl/pytorch/rodrigues_layer.py) [part of the code](https://github.com/MandyMo/pytorch_HMR/blob/master/src/util.py) by [Zhang Xiong](https://github.com/MandyMo) to compute the rotation utilities. + +If you find this code useful for your research, please cite the original [SMPL](http://smpl.is.tue.mpg.de) publication: + +``` +@article{SMPL:2015, + author = {Loper, Matthew and Mahmood, Naureen and Romero, Javier and Pons-Moll, Gerard and Black, Michael J.}, + title = {{SMPL}: A Skinned Multi-Person Linear Model}, + journal = {ACM Trans. Graphics (Proc. SIGGRAPH Asia)}, + number = {6}, + pages = {248:1--248:16}, + volume = {34}, + year = {2015} +} +``` + +## References + +\[1\] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black, "SMPL: A Skinned Multi-Person Linear Model," SIGGRAPH Asia, 2015. + +\[2\] Javier Romero, Dimitrios Tzionas, and Michael J. Black, "Embodied Hands: Modeling and Capturing Hands and Bodies Together," SIGGRAPH Asia, 2017. diff --git a/demo.py b/demo.py new file mode 100644 index 0000000..20252a6 --- /dev/null +++ b/demo.py @@ -0,0 +1,38 @@ +import torch + +from smpl.pytorch.smpl_layer import SMPL_Layer +from display_utils import display_model + + +if __name__ == '__main__': + cuda = False + batch_size = 1 + + # Create the SMPL layer + smpl_layer = SMPL_Layer( + center_idx=0, + gender='neutral', + model_root='smpl/native/models') + + # Generate random pose and shape parameters + pose_params = torch.rand(batch_size, 72) * 0.2 + shape_params = torch.rand(batch_size, 10) * 0.03 + + # GPU mode + if cuda: + pose_params = pose_params.cuda() + shape_params = shape_params.cuda() + smpl_layer.cuda() + + # Forward from the SMPL layer + verts, Jtr = smpl_layer(pose_params, th_betas=shape_params) + + # Draw output vertices and joints + display_model( + {'verts': verts.cpu().detach(), + 'joints': Jtr.cpu().detach()}, + model_faces=smpl_layer.th_faces, + with_joints=True, + kintree_table=smpl_layer.kintree_table, + savepath='image.png', + show=True) diff --git a/display_utils.py b/display_utils.py new file mode 100644 index 0000000..8e4f7dd --- /dev/null +++ b/display_utils.py @@ -0,0 +1,71 @@ +from matplotlib import pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from mpl_toolkits.mplot3d.art3d import Poly3DCollection +# plt.switch_backend('agg') + + +def display_model( + model_info, + model_faces=None, + with_joints=False, + kintree_table=None, + ax=None, + batch_idx=0, + show=True, + savepath=None): + """ + Displays mesh batch_idx in batch of model_info, model_info as returned by + generate_random_model + """ + if ax is None: + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + verts, joints = model_info['verts'][batch_idx], model_info['joints'][ + batch_idx] + if model_faces is None: + ax.scatter(verts[:, 0], verts[:, 1], verts[:, 2], alpha=0.2) + else: + mesh = Poly3DCollection(verts[model_faces], alpha=0.2) + face_color = (141 / 255, 184 / 255, 226 / 255) + edge_color = (50 / 255, 50 / 255, 50 / 255) + mesh.set_edgecolor(edge_color) + mesh.set_facecolor(face_color) + ax.add_collection3d(mesh) + if with_joints: + draw_skeleton(joints, kintree_table=kintree_table, ax=ax) + ax.set_xlabel('X') + ax.set_ylabel('Y') + ax.set_zlabel('Z') + ax.set_xlim(-0.7, 0.7) + ax.set_ylim(-0.7, 0.7) + ax.set_zlim(-0.7, 0.7) + ax.view_init(azim=-90, elev=100) + fig.subplots_adjust(left=0, right=1, bottom=0, top=1) + if savepath: + plt.savefig(savepath, bbox_inches='tight', pad_inches=0) + if show: + plt.show() + return ax + + +def draw_skeleton(joints3D, kintree_table, ax=None): + if ax is None: + fig = plt.figure(frameon=False) + ax = fig.add_subplot(111, projection='3d') + else: + ax = ax + + colors = [] + left_right_mid = ['r', 'g', 'b'] + kintree_colors = [2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 0, 1, 0, 1, 0, 1] + for c in kintree_colors: + colors += left_right_mid[c] + # For each 24 joint + for i in range(1, kintree_table.shape[1]): + j1 = kintree_table[0][i] + j2 = kintree_table[1][i] + ax.plot([joints3D[j1, 0], joints3D[j2, 0]], + [joints3D[j1, 1], joints3D[j2, 1]], + [joints3D[j1, 2], joints3D[j2, 2]], + color=colors[i], linestyle='-', linewidth=2, marker='o', markersize=5) + return ax diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..92378e0 --- /dev/null +++ b/environment.yml @@ -0,0 +1,10 @@ +name: smplpytorch + +dependencies: + - opencv + - python=3.7 + - matplotlib + - numpy + - pytorch + - pip: + - git+https://github.com/hassony2/chumpy.git diff --git a/image.png b/image.png new file mode 100644 index 0000000..3adc8ff Binary files /dev/null and b/image.png differ diff --git a/smpl/native/__init__.py b/smpl/native/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/smpl/native/models/README.md b/smpl/native/models/README.md new file mode 100644 index 0000000..9e113e9 --- /dev/null +++ b/smpl/native/models/README.md @@ -0,0 +1 @@ +Here copy the .pkl model files. diff --git a/smpl/native/webuser/__init__.py b/smpl/native/webuser/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/smpl/native/webuser/posemapper.py b/smpl/native/webuser/posemapper.py new file mode 100644 index 0000000..88a2ed7 --- /dev/null +++ b/smpl/native/webuser/posemapper.py @@ -0,0 +1,31 @@ +import chumpy as ch +import numpy as np +import cv2 + + +class Rodrigues(ch.Ch): + dterms = 'rt' + + def compute_r(self): + return cv2.Rodrigues(self.rt.r)[0] + + def compute_dr_wrt(self, wrt): + if wrt is self.rt: + return cv2.Rodrigues(self.rt.r)[1].T + + +def lrotmin(p): + if isinstance(p, np.ndarray): + p = p.ravel()[3:] + return np.concatenate([(cv2.Rodrigues(np.array(pp))[0] - np.eye(3)).ravel() for pp in p.reshape((-1, 3))]).ravel() + if p.ndim != 2 or p.shape[1] != 3: + p = p.reshape((-1, 3)) + p = p[1:] + return ch.concatenate([(Rodrigues(pp) - ch.eye(3)).ravel() for pp in p]).ravel() + + +def posemap(s): + if s == 'lrotmin': + return lrotmin + else: + raise Exception('Unknown posemapping: %s' % (str(s),)) diff --git a/smpl/native/webuser/serialization.py b/smpl/native/webuser/serialization.py new file mode 100644 index 0000000..a186430 --- /dev/null +++ b/smpl/native/webuser/serialization.py @@ -0,0 +1,39 @@ +def ready_arguments(fname_or_dict): + import numpy as np + import pickle + import chumpy as ch + from chumpy.ch import MatVecMult + from smpl.native.webuser.posemapper import posemap + + if not isinstance(fname_or_dict, dict): + dd = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1') + # dd = pickle.load(open(fname_or_dict, 'rb')) + else: + dd = fname_or_dict + + want_shapemodel = 'shapedirs' in dd + nposeparms = dd['kintree_table'].shape[1] * 3 + + if 'trans' not in dd: + dd['trans'] = np.zeros(3) + if 'pose' not in dd: + dd['pose'] = np.zeros(nposeparms) + if 'shapedirs' in dd and 'betas' not in dd: + dd['betas'] = np.zeros(dd['shapedirs'].shape[-1]) + + for s in ['v_template', 'weights', 'posedirs', 'pose', 'trans', 'shapedirs', 'betas', 'J']: + if (s in dd) and not hasattr(dd[s], 'dterms'): + dd[s] = ch.array(dd[s]) + + if want_shapemodel: + dd['v_shaped'] = dd['shapedirs'].dot(dd['betas']) + dd['v_template'] + v_shaped = dd['v_shaped'] + J_tmpx = MatVecMult(dd['J_regressor'], v_shaped[:, 0]) + J_tmpy = MatVecMult(dd['J_regressor'], v_shaped[:, 1]) + J_tmpz = MatVecMult(dd['J_regressor'], v_shaped[:, 2]) + dd['J'] = ch.vstack((J_tmpx, J_tmpy, J_tmpz)).T + dd['v_posed'] = v_shaped + dd['posedirs'].dot(posemap(dd['bs_type'])(dd['pose'])) + else: + dd['v_posed'] = dd['v_template'] + dd['posedirs'].dot(posemap(dd['bs_type'])(dd['pose'])) + + return dd diff --git a/smpl/pytorch/__init__.py b/smpl/pytorch/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/smpl/pytorch/rodrigues_layer.py b/smpl/pytorch/rodrigues_layer.py new file mode 100644 index 0000000..46ae478 --- /dev/null +++ b/smpl/pytorch/rodrigues_layer.py @@ -0,0 +1,85 @@ +""" +This part reuses code from https://github.com/MandyMo/pytorch_HMR/blob/master/src/util.py +which is part of a PyTorch port of SMPL. +Thanks to Zhang Xiong (MandyMo) for making this great code available on github ! +""" + +import argparse +from torch.autograd import gradcheck +import torch +from torch.autograd import Variable + + +def quat2mat(quat): + """Convert quaternion coefficients to rotation matrix. + Args: + quat: size = [batch_size, 4] 4 <===>(w, x, y, z) + Returns: + Rotation matrix corresponding to the quaternion -- size = [batch_size, 3, 3] + """ + norm_quat = quat + norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True) + w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, + 2], norm_quat[:, + 3] + + batch_size = quat.size(0) + + w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2) + wx, wy, wz = w * x, w * y, w * z + xy, xz, yz = x * y, x * z, y * z + + rotMat = torch.stack([ + w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, 2 * wz + 2 * xy, + w2 - x2 + y2 - z2, 2 * yz - 2 * wx, 2 * xz - 2 * wy, 2 * wx + 2 * yz, + w2 - x2 - y2 + z2 + ], + dim=1).view(batch_size, 3, 3) + return rotMat + + +def batch_rodrigues(axisang): + #axisang N x 3 + axisang_norm = torch.norm(axisang + 1e-8, p=2, dim=1) + angle = torch.unsqueeze(axisang_norm, -1) + axisang_normalized = torch.div(axisang, angle) + angle = angle * 0.5 + v_cos = torch.cos(angle) + v_sin = torch.sin(angle) + quat = torch.cat([v_cos, v_sin * axisang_normalized], dim=1) + rot_mat = quat2mat(quat) + rot_mat = rot_mat.view(rot_mat.shape[0], 9) + return rot_mat + + +def th_get_axis_angle(vector): + angle = torch.norm(vector, 2, 1) + axes = vector / angle.unsqueeze(1) + return axes, angle + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--batch_size', default=1, type=int) + parser.add_argument('--cuda', action='store_true') + args = parser.parse_args() + + n_components = 6 + rot = 3 + inputs = torch.rand(args.batch_size, rot) + inputs_var = Variable(inputs.double(), requires_grad=True) + if args.cuda: + inputs = inputs.cuda() + # outputs = batch_rodrigues(inputs) + test_function = gradcheck(batch_rodrigues, (inputs_var, )) + print('batch test passed !') + + inputs = torch.rand(rot) + inputs_var = Variable(inputs.double(), requires_grad=True) + test_function = gradcheck(th_cv2_rod_sub_id.apply, (inputs_var, )) + print('th_cv2_rod test passed') + + inputs = torch.rand(rot) + inputs_var = Variable(inputs.double(), requires_grad=True) + test_th = gradcheck(th_cv2_rod.apply, (inputs_var, )) + print('th_cv2_rod_id test passed !') diff --git a/smpl/pytorch/smpl_layer.py b/smpl/pytorch/smpl_layer.py new file mode 100644 index 0000000..9ab4cb3 --- /dev/null +++ b/smpl/pytorch/smpl_layer.py @@ -0,0 +1,152 @@ +import os + +import numpy as np +import torch +from torch.nn import Module + +from smpl.native.webuser.serialization import ready_arguments +from smpl.pytorch import rodrigues_layer +from smpl.pytorch.tensutils import (th_posemap_axisang, th_with_zeros, th_pack, make_list) + + +class SMPL_Layer(Module): + __constants__ = ['kintree_parents', 'gender', 'center_idx', 'num_joints'] + + def __init__(self, + center_idx=None, + gender='neutral', + model_root='smpl/native/models'): + """ + Args: + center_idx: index of center joint in our computations, + model_root: path to pkl files for the model + gender: 'neutral' (default) or 'female' or 'male' + """ + super().__init__() + + self.center_idx = center_idx + self.gender = gender + + if gender == 'neutral': + self.model_path = os.path.join(model_root, 'basicModel_neutral_lbs_10_207_0_v1.0.0.pkl') + elif gender == 'female': + self.model_path = os.path.join(model_root, 'basicModel_f_lbs_10_207_0_v1.0.0.pkl') + elif gender == 'male': + self.model_path = os.path.join(model_root, 'basicModel_m_lbs_10_207_0_v1.0.0.pkl') + + smpl_data = ready_arguments(self.model_path) + self.smpl_data = smpl_data + + self.register_buffer('th_betas', + torch.Tensor(smpl_data['betas'].r).unsqueeze(0)) + self.register_buffer('th_shapedirs', + torch.Tensor(smpl_data['shapedirs'].r)) + self.register_buffer('th_posedirs', + torch.Tensor(smpl_data['posedirs'].r)) + self.register_buffer( + 'th_v_template', + torch.Tensor(smpl_data['v_template'].r).unsqueeze(0)) + self.register_buffer( + 'th_J_regressor', + torch.Tensor(np.array(smpl_data['J_regressor'].toarray()))) + self.register_buffer('th_weights', + torch.Tensor(smpl_data['weights'].r)) + self.register_buffer('th_faces', + torch.Tensor(smpl_data['f'].astype(np.int32)).long()) + + # Kinematic chain params + self.kintree_table = smpl_data['kintree_table'] + parents = list(self.kintree_table[0].tolist()) + self.kintree_parents = parents + self.num_joints = len(parents) # 24 + + def forward(self, + th_pose_coeffs, + th_betas=torch.zeros(1), + th_trans=torch.zeros(1)): + """ + Args: + th_betas (Tensor (batch_size x 10)): if provided, uses given shape parameters + th_trans (Tensor (batch_size x 3)): if provided, applies trans to joints and vertices + """ + + batch_size = th_pose_coeffs.shape[0] + th_pose_map, th_rot_map = th_posemap_axisang(th_pose_coeffs) + th_pose_coeffs = th_pose_coeffs.view(batch_size, -1, 3) + root_rot = rodrigues_layer.batch_rodrigues( + th_pose_coeffs[:, 0]).view(batch_size, 3, 3) + + if th_betas is None or bool(torch.norm(th_betas) == 0): + th_v_shaped = torch.matmul(self.th_shapedirs, + self.th_betas.transpose(1, 0)).permute( + 2, 0, 1) + self.th_v_template + th_j = torch.matmul(self.th_J_regressor, th_v_shaped).repeat( + batch_size, 1, 1) + else: + th_v_shaped = torch.matmul(self.th_shapedirs, + th_betas.transpose(1, 0)).permute( + 2, 0, 1) + self.th_v_template + th_j = torch.matmul(self.th_J_regressor, th_v_shaped) + + th_v_posed = th_v_shaped + torch.matmul( + self.th_posedirs, th_pose_map.transpose(0, 1)).permute(2, 0, 1) + # Final T pose with transformation done ! + + # Global rigid transformation + th_results = [] + + root_j = th_j[:, 0, :].contiguous().view(batch_size, 3, 1) + th_results.append(th_with_zeros(torch.cat([root_rot, root_j], 2))) + + # Rotate each part + for i in range(self.num_joints - 1): + i_val = int(i + 1) + joint_rot = th_rot_map[:, (i_val - 1) * 9:i_val * + 9].contiguous().view(batch_size, 3, 3) + joint_j = th_j[:, i_val, :].contiguous().view(batch_size, 3, 1) + parent = make_list(self.kintree_parents)[i_val] + parent_j = th_j[:, parent, :].contiguous().view(batch_size, 3, 1) + joint_rel_transform = th_with_zeros( + torch.cat([joint_rot, joint_j - parent_j], 2)) + th_results.append( + torch.matmul(th_results[parent], joint_rel_transform)) + th_results_global = th_results + + th_results2 = torch.zeros((batch_size, 4, 4, self.num_joints), + dtype=root_j.dtype, + device=root_j.device) + + for i in range(self.num_joints): + padd_zero = torch.zeros(1, dtype=th_j.dtype, device=th_j.device) + joint_j = torch.cat( + [th_j[:, i], + padd_zero.view(1, 1).repeat(batch_size, 1)], 1) + tmp = torch.bmm(th_results[i], joint_j.unsqueeze(2)) + th_results2[:, :, :, i] = th_results[i] - th_pack(tmp) + + th_T = torch.matmul(th_results2, self.th_weights.transpose(0, 1)) + + th_rest_shape_h = torch.cat([ + th_v_posed.transpose(2, 1), + torch.ones((batch_size, 1, th_v_posed.shape[1]), + dtype=th_T.dtype, + device=th_T.device), + ], 1) + + th_verts = (th_T * th_rest_shape_h.unsqueeze(1)).sum(2).transpose(2, 1) + th_verts = th_verts[:, :, :3] + th_jtr = torch.stack(th_results_global, dim=1)[:, :, :3, 3] + + if th_trans is None or bool(torch.norm(th_trans) == 0): + if self.center_idx is not None: + center_joint = th_jtr[:, self.center_idx].unsqueeze(1) + th_jtr = th_jtr - center_joint + th_verts = th_verts - center_joint + else: + th_jtr = th_jtr + th_trans.unsqueeze(1) + th_verts = th_verts + th_trans.unsqueeze(1) + + # Scale to milimeters + # th_verts = th_verts * 1000 + # th_jtr = th_jtr * 1000 + return th_verts, th_jtr diff --git a/smpl/pytorch/tensutils.py b/smpl/pytorch/tensutils.py new file mode 100644 index 0000000..5d07bbc --- /dev/null +++ b/smpl/pytorch/tensutils.py @@ -0,0 +1,52 @@ +import torch + +from smpl.pytorch import rodrigues_layer + + +def th_posemap_axisang(pose_vectors): + rot_nb = int(pose_vectors.shape[1] / 3) + rot_mats = [] + for joint_idx in range(rot_nb - 1): + joint_idx_val = joint_idx + 1 + axis_ang = pose_vectors[:, joint_idx_val * 3:(joint_idx_val + 1) * 3] + rot_mat = rodrigues_layer.batch_rodrigues(axis_ang) + rot_mats.append(rot_mat) + + # rot_mats = torch.stack(rot_mats, 1).view(-1, 15 *9) + rot_mats = torch.cat(rot_mats, 1) + pose_maps = subtract_flat_id(rot_mats) + return pose_maps, rot_mats + + +def th_with_zeros(tensor): + batch_size = tensor.shape[0] + padding = tensor.new([0.0, 0.0, 0.0, 1.0]) + padding.requires_grad = False + + concat_list = [tensor, padding.view(1, 1, 4).repeat(batch_size, 1, 1)] + cat_res = torch.cat(concat_list, 1) + return cat_res + + +def th_pack(tensor): + batch_size = tensor.shape[0] + padding = tensor.new_zeros((batch_size, 4, 3)) + padding.requires_grad = False + pack_list = [padding, tensor] + pack_res = torch.cat(pack_list, 2) + return pack_res + + +def subtract_flat_id(rot_mats): + # Subtracts identity as a flattened tensor + id_flat = torch.eye( + 3, dtype=rot_mats.dtype, device=rot_mats.device).view(1, 9).repeat( + rot_mats.shape[0], 23) + # id_flat.requires_grad = False + results = rot_mats - id_flat + return results + + +def make_list(tensor): + # type: (List[int]) -> List[int] + return tensor