diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e3794f1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +venv +results +trained_models/* +assets/CMU/* +assets/SMPL/* +__pycache__ \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..70fa7ee --- /dev/null +++ b/README.md @@ -0,0 +1,90 @@ +# Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On + +![Teaser](assets/images/teaser.jpg "Teaser image") + +[[Project website](http://mslab.es/projects/SelfSupervisedGarmentCollisions/)] [[Dataset](https://github.com/isantesteban/vto-dataset)] [[Video](https://youtu.be/9AnBNco6i2U)] + +## Abstract + +>We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions. In contrast to existing methods that require an undesirable postprocessing step to fix garment-body interpenetrations at test time, our approach directly outputs 3D garment configurations that do not collide with the underlying body. Key to our success is a new canonical space for garments that removes pose-and-shape deformations already captured by a new diffused human body model, which extrapolates body surface properties such as skinning weights and blendshapes to any 3D point. We leverage this representation to train a generative model with a novel self-supervised collision term that learns to reliably solve garment-body interpenetrations. We extensively evaluate and compare our results with recently proposed data-driven methods, and show that our method is the first to successfully address garment-body contact in unseen body shapes and motions, without compromising realism and detail. + + +# Running the model + +**Requirements**: ```python3.8```, ```tensorflow-2.2.1```, ```numpy-1.18.5```, ```scipy-1.7.1```, ```chumpy-0.70``` + +**Project structure**: +``` +vto-garment-collisions +│ +└───assets +| └─ images +| └─ meshes +| └─ CMU # Not included, see instructions +| └─ SMPL # Not included, see instructions +| +└───rendering # Code to render meshes +| +└───src # Code to run the model +| +└───trained_models +| └─ diffused_body # Networks of the diffused body model (Not included, see instructions) +| └─ tshirt # Networks of tshirt model (Not included, see instructions) +│ +└───run_model.py +``` + +## Download trained models + +1. Download models of the diffused human body: https://github.com/isantesteban/vto-garment-collisions/releases/download/trained-models/trained_models_diffused_body.zip +2. Download models of the garment: https://github.com/isantesteban/vto-garment-collisions/releases/download/tshirt-trained-models/trained_models_tshirt.zip +3. Create ```trained_models``` directory and extract ```trained_models_diffused_body.zip``` and ```trained_models_tshirt.zip``` there. + +## Download human model + +1. Sign in into https://smpl.is.tue.mpg.de +2. Download SMPL version 1.0.0 for Python 2.7 (10 shape PCs) +3. Extract ```SMPL_python_v.1.0.0.zip``` and copy ```smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl``` in ```assets/SMPL``` + +## Download animation sequences + +1. Sign in into https://amass.is.tue.mpg.de +2. Download the body data for the CMU motions (SMPL+H model) +3. Extract ```CMU.tar.bz2``` in ```assets/CMU```: +```sh +tar -C assets/ -xf ~/Downloads/CMU.tar.bz2 CMU/ +``` + +## Generate garment animation + +To generate the deformed garment meshes for a given sequence: + +```sh +python run_model.py assets/CMU/07/07_02_poses.npz --export_dir results/07_02 +``` + + +# Rendering +**Requirements**: ```blender-2.93```, ```ffmpeg``` + +To render the meshes: + +```sh +blender --background rendering/scene.blend --python rendering/render.py --path results/07_02 +``` + +![Render](assets/images/render.gif "Video rendered by Blender") + +# Citation + +If you find this repository useful please cite our work: + +``` +@article {santesteban2021garmentcollisions, + journal = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + title = {{Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On}}, + author = {Santesteban, Igor and Thuerey, Nils and Otaduy, Miguel A and Casas, Dan}, + year = {2021} +} +``` + diff 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\ No newline at end of file diff --git a/rendering/render.py b/rendering/render.py new file mode 100755 index 0000000..b50e03c --- /dev/null +++ b/rendering/render.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python +import sys +import os +import glob + +sys.path.append(".") + +from rendering.renderer import GarmentRenderer + +try: + index = sys.argv.index("--path") + path = sys.argv[index + 1] +except ValueError: + print("Usage: blender --background rendering/scene.blend --python rendering/render.py --path ") + + +renderer = GarmentRenderer( + cloth_paths=sorted(glob.glob(os.path.join(path, "*garment.obj"))), + body_paths=sorted(glob.glob(os.path.join(path, "*body.obj"))), + cloth_material="ClothMaterialYellow", + body_material="MannequinMaterialDark", + export_path=os.path.join(path, "render") +) + +renderer.render(resolution_percentage=100, fov=50, start_frame=0, end_frame=None) +renderer.generate_video() + diff --git a/rendering/renderer.py b/rendering/renderer.py new file mode 100644 index 0000000..b303f2b --- /dev/null +++ b/rendering/renderer.py @@ -0,0 +1,214 @@ +import bpy +import os + +import numpy as np + + +class GarmentRenderer: + + def __init__(self, cloth_paths, body_paths, **config): + self.cloth_paths = cloth_paths + self.body_paths = body_paths + + # Blender scene + self.camera = bpy.context.scene.camera + self.camera_tracking = config.pop("camera_tracking", True) + + self.render_settings = bpy.context.scene.render + self.render_body = config.pop("render_body", True) + self.render_cloth = config.pop("render_cloth", True) + self.follow_body = config.pop("follow_body", False) + + self.cloth_material = config.pop("cloth_material", "ClothMaterialPurple") + self.body_material = config.pop("body_material", "BodyMaterial") + self.rotation = config.pop("rotation", 0.0) + self.translation = config.pop("translation", [0.0, 0.0, 0.0]) + + # Export configuration + self.export_path = config.pop("export_path", "tmp/render") + self.suffix = config.pop("suffix", "") + self.prefix = config.pop("prefix", "") + self.digits = config.pop("digits", 5) + self.extension = ".jpg" + + for key in config.keys(): + raise ValueError("Unexpected argument '" + key + "'") + + self.initialized = False + self.initial_position = None + + + def initialize(self): + if self.initialized: + return + + self.frames = max(len(self.cloth_paths), len(self.body_paths)) + + # Create the export directory if does not exist + if not os.path.exists(self.export_path): + os.makedirs(self.export_path) + + self.initialized = True + + + def disable_tracking(self): + self.camera.constraints["Copy Location"].target = None + self.camera.constraints["Track To"].target = None + self.camera_tracking = False + + + def track_object(self, mesh): + if not self.camera_tracking: + return + + self.camera.constraints["Copy Location"].target = mesh + self.camera.constraints["Track To"].target = mesh + + + def get_body(self, frame): + return self.load_obj(self.body_paths[frame], self.body_material) + + + def get_cloth(self, frame): + cloth = self.load_obj(self.cloth_paths[frame], self.cloth_material) + # self.add_wireframe(cloth, thickness=0.0005) + return cloth + + + def add_wireframe(self, obj, thickness = 0.001): + obj.data.materials.append(bpy.data.materials["WireframeMaterial"]) + obj.modifiers.new(type='WIREFRAME', name="Wireframe") + obj.modifiers["Wireframe"].thickness = thickness + obj.modifiers["Wireframe"].material_offset = 1 + obj.modifiers["Wireframe"].use_replace = False + + + def load_obj(self, path, material): + bpy.ops.import_scene.obj(filepath=path, split_mode="OFF") + bpy.ops.object.origin_set(type='ORIGIN_GEOMETRY') + bpy.ops.object.shade_smooth() + + mesh = bpy.context.selected_objects[0] + mesh.active_material = bpy.data.materials[material] + + return mesh + + + def delete_object(self, mesh): + if mesh == None: + return + + bpy.ops.object.select_all(action='DESELECT') + mesh.select_set(state=True) + bpy.ops.object.delete() + + + def render_path(self, frame): + file_name = self.prefix + str(frame).zfill(self.digits) + self.suffix + self.extension + return os.path.join(self.export_path, file_name) + + + def render(self, resolution_x = 1920, resolution_y = 1080, fov = 45, resolution_percentage = 100, start_frame = 0, end_frame = None): + self.initialize() + + # Set render settings + self.render_settings.resolution_x = resolution_x + self.render_settings.resolution_y = resolution_y + self.render_settings.resolution_percentage = resolution_percentage + self.camera.data.sensor_width = max(fov, 25) + + body_mesh = None + cloth_mesh = None + + # Start rendering + for frame in range(self.frames): + + if frame < start_frame: + continue + + if end_frame is not None and frame > end_frame: + break + + print("\n[ INFO ] Rendering frame %d" % frame) + + # Get the objects of the current frame + if self.render_body and frame < len(self.body_paths): + self.delete_object(body_mesh) + body_mesh = self.get_body(frame) + + if frame < len(self.cloth_paths): + self.delete_object(cloth_mesh) + cloth_mesh = self.get_cloth(frame) + + # Make the camera look at the cloth (or the body if there is no cloth) + if cloth_mesh != None and not self.follow_body: + self.track_object(cloth_mesh) + + if body_mesh != None and self.follow_body: + self.track_object(body_mesh) + + # Rotate the cloth + cloth_mesh.parent = body_mesh + cloth_mesh.matrix_parent_inverse = body_mesh.matrix_world.inverted() + body_mesh.rotation_euler[2] = self.rotation + + # Render the frame + self.render_settings.filepath = self.render_path(frame) + bpy.ops.render.render(write_still = True) + + self.delete_object(body_mesh) + self.delete_object(cloth_mesh) + + + def generate_video(self, fps = 30, crf = 20, name="output"): + file_pattern = self.prefix + "%0" + str(self.digits) + "d" + self.suffix + self.extension + + command = "ffmpeg" + command += " -i " + os.path.join(self.export_path, file_pattern) + command += " -c:v libx264 -profile:v high -pix_fmt yuv420p" + command += " -framerate " + str(fps) + command += " -crf " + str(crf) + command += " -y " + os.path.join(self.export_path, name + ".mp4") + + os.system(command) + + # Convert jpg sequence to video + # ffmpeg -framerate 30 -i %05d.jpg -c:v libx264 -profile:v high -crf 10 -pix_fmt yuv420p output.mp4 + +def read_obj(file_name, read_uvs=False): + # Counts number of vertices and faces + vertices = [] + faces = [] + uvs = [] + faces_uv = [] + + with open(file_name, 'r') as fp: + for line in fp: + line_split = line.split() + + if not line_split: + continue + + if read_uvs and line_split[0] == 'vt': + uvs.append([line_split[1], line_split[2]]) + + if line_split[0] == 'v': + vertices.append([line_split[1], line_split[2], line_split[3]]) + + if line_split[0] == 'f': + vertex_indices = [s.split("/")[0] for s in line_split[1:]] + faces.append(vertex_indices) + + if read_uvs: + uv_indices = [s.split("/")[1] for s in line_split[1:]] + faces_uv.append(uv_indices) + + vertices = np.array(vertices, dtype=np.float32) + faces = np.array(faces, dtype=np.int32) - 1 + + if read_uvs: + uvs = np.array(uvs, dtype=np.float32) + faces_uv = np.array(faces_uv, dtype=np.int32) - 1 + return vertices, faces, uvs, faces_uv + + return vertices, faces \ No newline at end of file diff --git a/rendering/scene.blend b/rendering/scene.blend new file mode 100644 index 0000000..ed0c6db Binary files /dev/null and b/rendering/scene.blend differ diff --git a/run_model.py b/run_model.py new file mode 100644 index 0000000..7631c18 --- /dev/null +++ b/run_model.py @@ -0,0 +1,45 @@ +import argparse +import os + +from src.io import * +from src.model import * + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Run the garment deformation model" + ) + + parser.add_argument( + "motion_path", + type=str, + help="path to the .npz file with the motion data" + ) + + parser.add_argument( + "--export_dir", + type=str, + default="results", + help="directory to save the predictions" + ) + + args = parser.parse_args() + + v_garment, v_body = run_model( + model_dict=load_model(), + motion=load_motion(args.motion_path) + ) + + # Save meshes + os.makedirs(args.export_dir, exist_ok=True) + + _, f_garment = load_obj("assets/meshes/tshirt.obj") + _, f_body = load_obj("assets/meshes/body.obj") + + for i in range(len(v_garment)): + path = os.path.join(args.export_dir, f"{i:04d}_body.obj") + save_obj(path, v_body[i], f_body) + + path = os.path.join(args.export_dir, f"{i:04d}_garment.obj") + save_obj(path, v_garment[i], f_garment) + diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/io.py b/src/io.py new file mode 100644 index 0000000..a181691 --- /dev/null +++ b/src/io.py @@ -0,0 +1,86 @@ +import os + +import numpy as np +import tensorflow as tf +from scipy.spatial.transform import Rotation as R + + +def load_motion(path, separate_arms=True): + motion_dict = dict(np.load(path)) + + # The recurrent regressor is trained with 30fps sequences + target_fps = 30 + drop_factor = int(motion_dict["mocap_framerate"] // target_fps) + + trans = motion_dict["trans"][::drop_factor] + poses = motion_dict["poses"][::drop_factor, :72] + shape = motion_dict["betas"][:10] + + # Separate arms + if separate_arms: + angle = 15 + left_arm = 17 + right_arm = 16 + + poses = poses.reshape((-1, poses.shape[-1] // 3, 3)) + rot = R.from_euler('z', -angle, degrees=True) + poses[:, left_arm] = (rot * R.from_rotvec(poses[:, left_arm])).as_rotvec() + rot = R.from_euler('z', angle, degrees=True) + poses[:, right_arm] = (rot * R.from_rotvec(poses[:, right_arm])).as_rotvec() + + poses = poses.reshape((poses.shape[0], -1)) + + # Swap axes + rotation = R.from_euler("zx", [-90, 270], degrees=True) + root_rotation = R.from_rotvec(poses[:, :3]) + poses[:, :3] = (rotation * root_rotation).as_rotvec() + trans = rotation.apply(trans) + + # Remove hand rotation + poses[:, 66:] = 0 + + # Center model in first frame + trans = trans - trans[0] + + return { + "pose": poses.astype(np.float32), + "shape": shape.astype(np.float32), + "translation": trans.astype(np.float32), + } + + +def load_obj(filename): + vertices = [] + faces = [] + + with open(filename, 'r') as fp: + for line in fp: + line_split = line.split() + + if not line_split: + continue + + elif line_split[0] == 'v': + vertices.append([line_split[1], line_split[2], line_split[3]]) + + elif line_split[0] == 'f': + vertex_indices = [s.split("/")[0] for s in line_split[1:]] + faces.append(vertex_indices) + + vertices = np.array(vertices, dtype=np.float32) + faces = np.array(faces, dtype=np.int32) - 1 + + return vertices, faces + + +def save_obj(filename, vertices, faces): + os.makedirs(os.path.dirname(filename), exist_ok=True) + + with open(filename, 'w') as fp: + for v in vertices: + fp.write('v %f %f %f\n' % (v[0], v[1], v[2])) + + for f in (faces + 1): # Faces are 1-based, not 0-based in obj files + fp.write('f %d %d %d\n' % (f[0], f[1], f[2])) + + print("Saved:", filename) diff --git a/src/math.py b/src/math.py new file mode 100644 index 0000000..b09bc65 --- /dev/null +++ b/src/math.py @@ -0,0 +1,78 @@ +import numpy as np +import tensorflow as tf +import tensorflow.keras as keras + + +class AxisAngleToMatrix(keras.layers.Layer): + def __init__(self, **kwargs): + super(AxisAngleToMatrix, self).__init__(**kwargs) + + + def call(self, axis_angle): + """Converts rotations in axis-angle representation to rotation matrices + + Args: + axis_angle: tensor of shape batch_size x 3 + + Returns: + rotation_matrix: tensor of shape batch_size x 3 x 3 + """ + initial_shape = tf.shape(axis_angle) + + axis_angle = tf.reshape(axis_angle, [-1, 3]) + batch_size = tf.shape(axis_angle)[0] + + angle = tf.expand_dims(tf.norm(axis_angle + 1e-8, axis=1), -1) + axis = tf.expand_dims(tf.math.divide(axis_angle, angle), -1) + + angle = tf.expand_dims(angle, -1) + cos = tf.cos(angle) + sin = tf.sin(angle) + + outer = tf.matmul(axis, axis, transpose_b=True, name="outer") + + eyes = tf.tile(tf.expand_dims(tf.eye(3), 0), [batch_size, 1, 1]) + R = cos * eyes + (1 - cos) * outer + sin * Skew()(axis) + R = tf.reshape(R, tf.concat([initial_shape, [3]], axis=0)) + + return R + + +class Skew(keras.layers.Layer): + def __init__(self, **kwargs): + super(Skew, self).__init__(**kwargs) + + + def call(self, vec): + """Returns the skew symetric version of each 3x3 matrix in a vector + + Args: + vec: tensor of shape batch_size x 3 + + Returns: + rotation_matrix: tensor of shape batch_size x 3 x 3 + """ + batch_size = tf.shape(vec)[0] + + col_inds = tf.constant([1, 2, 3, 5, 6, 7]) + batch_inds = tf.reshape(tf.range(0, batch_size) * 9, [-1, 1]) + indices = tf.reshape(batch_inds + col_inds, [-1, 1]) + + updates = tf.stack( + values=[-vec[:, 2], vec[:, 1], vec[:, 2], + -vec[:, 0], -vec[:, 1], vec[:, 0]], + axis=1 + ) + updates = tf.reshape(updates, [-1]) + + out_shape = [batch_size * 9] + res = tf.scatter_nd(indices, updates, out_shape) + res = tf.reshape(res, [batch_size, 3, 3]) + + return res + + +def finite_diff(x, h): + v = np.zeros(x.shape) + v[1:] = (x[1:] - x[0:-1]) / h + return v diff --git a/src/model.py b/src/model.py new file mode 100644 index 0000000..a3ab1bb --- /dev/null +++ b/src/model.py @@ -0,0 +1,124 @@ +import numpy as np +import tensorflow as tf +from scipy.spatial.transform import Rotation as R + +from . import math +from . import skinning +from . import smpl + + +def load_model(): + return { + "smpl": smpl.SMPL( + "assets/SMPL/basicModel_f_lbs_10_207_0_v1.0.0.pkl" + ), + + "body/pose_encoder": tf.keras.models.load_model( + "trained_models/diffused_body/pose_encoder", + compile=False + ), + + "body/skinning_weights": tf.keras.models.load_model( + "trained_models/diffused_body/skinning_weights", + compile=False + ), + + "body/pose_blendshape": tf.keras.models.load_model( + "trained_models/diffused_body/pose_blendshape", + compile=False + ), + + "body/shape_blendshape": tf.keras.models.load_model( + "trained_models/diffused_body/shape_blendshape", + compile=False + ), + + "tshirt/gru": tf.keras.models.load_model( + "trained_models/tshirt/gru", + compile=False + ), + + "tshirt/decoder": tf.keras.models.load_model( + "trained_models/tshirt/decoder", + compile=False + ) + } + + +def run_model(model_dict, motion): + ''' + This function evaluates the runtime pipeline step by step. + + Note: to run our model at interactive framerates we wrap + this code into a custom Keras model and use TensorRT to optimize + the computational graph. We provide the unoptimized code because + despite being slower it's much clearer and shows all the computations + involved in our method (and doesn't require additional dependencies). + ''' + + num_frames = len(motion["pose"]) + + # Run pose encoder + motion["pose_encoded"] = model_dict["body/pose_encoder"].predict( + motion["pose"][:, 3:] + ) + + # Compute velocities and accelerations for input vector + h = 1.0 / 30.0 # 30 fps + motion['translation_vel'] = math.finite_diff(motion['translation'], h) + motion['translation_acc'] = math.finite_diff(motion['translation_vel'], h) + + rot = R.from_rotvec(motion['pose'][:,:3]) + motion['euler_angles'] = rot.as_euler('zxy') + motion['euler_angles_vel'] = math.finite_diff(motion['euler_angles'], h) + motion['euler_angles_acc'] = math.finite_diff(motion['euler_angles_vel'], h) + + motion['pose_encoded_vel'] = math.finite_diff(motion['pose_encoded'], h) + motion['pose_encoded_acc'] = math.finite_diff(motion['pose_encoded_vel'], h) + + shape = np.tile(motion["shape"], (num_frames, 1)) + + # Run model + print("[INFO] Run recurrent regressor...") + v_encoded = model_dict["tshirt/gru"].predict({ + 'shape': np.expand_dims(shape, axis=0), + 'pose_encoded': np.expand_dims(motion['pose_encoded'], axis=0), + 'pose_encoded_vel': np.expand_dims(motion['pose_encoded_vel'], axis=0), + 'pose_encoded_acc': np.expand_dims(motion['pose_encoded_acc'], axis=0), + 'translation_vel': np.expand_dims(motion['translation_vel'], axis=0), + 'translation_acc': np.expand_dims(motion['translation_acc'], axis=0), + 'euler_angles_vel': np.expand_dims(motion['euler_angles_vel'], axis=0), + 'euler_angles_acc': np.expand_dims(motion['euler_angles_acc'], axis=0), + })[0] + + print("[INFO] Project from latent space to canonical space...") + v_canonical = model_dict["tshirt/decoder"].predict(v_encoded) + v_canonical_flat = tf.reshape(v_canonical, (-1, 3)) + num_vertices = v_canonical.shape[-2] + + print("[INFO] Project from canonical space to unpose...") + v_body, smpl_dict = model_dict["smpl"](shape, motion["pose"]) + + pose_repeat = tf.repeat(smpl_dict['pose_feature'], num_vertices, axis=0) + pose_blendshape = model_dict["body/pose_blendshape"].predict([v_canonical_flat, pose_repeat]) + + shape_repeat = tf.repeat(shape, num_vertices, axis=0) + shape_blendshape = model_dict["body/shape_blendshape"].predict([v_canonical_flat, shape_repeat]) + + skinning_weights = model_dict["body/skinning_weights"].predict(v_canonical_flat) + skinning_weights = tf.reshape(skinning_weights, (num_frames, num_vertices, -1)) + + v_unpose = v_canonical_flat + pose_blendshape + shape_blendshape + v_unpose = tf.reshape(v_unpose, (num_frames, num_vertices, 3)) + + print("[INFO] Compute linear blend skinning...") + joint_transforms = smpl_dict['joint_transforms'] + v_garment = skinning.LBS()(v_unpose, joint_transforms, skinning_weights) + + # Add translation + v_body = v_body + motion["translation"][:, None, :] + v_garment = v_garment + motion["translation"][:, None, :] + + print("[INFO] Done!") + + return v_garment.numpy(), v_body.numpy() diff --git a/src/skinning.py b/src/skinning.py new file mode 100644 index 0000000..053e371 --- /dev/null +++ b/src/skinning.py @@ -0,0 +1,89 @@ +import tensorflow as tf +import tensorflow.keras as keras + + +class PoseSkeleton(keras.layers.Layer): + def __init__(self, **kwargs): + super(PoseSkeleton, self).__init__(**kwargs) + + + def call(self, joint_rotations, joint_positions, parents): + """ + Computes absolute joint locations given pose. + + Args: + joint_rotations: batch_size x K x 3 x 3 rotation vector of K joints + joint_positions: batch_size x K x 3, joint locations before posing + parents: vector of size K holding the parent id for each joint + + Returns + joint_transforms: `Tensor`: batch_size x K x 4 x 4 relative joint transformations for LBS. + joint_positions_posed: batch_size x K x 3, joint locations after posing + """ + batch_size = tf.shape(joint_rotations)[0] + num_joints = len(parents) + + def make_affine(rotation, translation, name=None): + ''' + Args: + rotation: batch_size x 3 x 3 + translation: batch_size x 3 x 1 + ''' + rotation_homo = tf.pad(rotation, [[0, 0], [0, 1], [0, 0]]) + translation_homo = tf.concat([translation, tf.ones([batch_size, 1, 1])], 1) + affine_transform = tf.concat([rotation_homo, translation_homo], 2) + return affine_transform + + joint_positions = tf.expand_dims(joint_positions, axis=-1) + root_rotation = joint_rotations[:, 0, :, :] + root_transform = make_affine(root_rotation, joint_positions[:, 0]) + + # Traverse joints to compute global transformations + transforms = [root_transform] + for joint, parent in enumerate(parents[1:], start=1): + position = joint_positions[:, joint] - joint_positions[:, parent] + transform_local = make_affine(joint_rotations[:, joint], position) + transform_global = tf.matmul(transforms[parent], transform_local) + transforms.append(transform_global) + transforms = tf.stack(transforms, axis=1) + + # Extract joint positions + joint_positions_posed = transforms[:, :, :3, 3] + + # Compute affine transforms relative to initial state (i.e., t-pose) + zeros = tf.zeros([batch_size, num_joints, 1, 1]) + joint_rest_positions = tf.concat([joint_positions, zeros], axis=2) + init_bone = tf.matmul(transforms, joint_rest_positions) + init_bone = tf.pad(init_bone, [[0, 0], [0, 0], [0, 0], [3, 0]]) + joint_transforms = transforms - init_bone + + return joint_transforms, joint_positions_posed + + +class LBS(keras.layers.Layer): + def __init__(self, **kwargs): + super(LBS, self).__init__(**kwargs) + + + def call(self, vertices, joint_rotations, skinning_weights): + skinning_weights = tf.convert_to_tensor(skinning_weights, self.dtype) + + batch_size = tf.shape(vertices)[0] + num_joints = skinning_weights.shape[-1] + num_vertices = vertices.shape[-2] + + W = skinning_weights + if len(skinning_weights.shape.as_list()) < len(vertices.shape.as_list()): + W = tf.tile(tf.convert_to_tensor(skinning_weights), [batch_size, 1]) + W = tf.reshape(W, [batch_size, skinning_weights.shape[-2], num_joints]) + + A = tf.reshape(joint_rotations, (-1, num_joints, 16)) + T = tf.matmul(W, A) + T = tf.reshape(T, (-1, num_vertices, 4, 4)) + + ones = tf.ones([batch_size, num_vertices, 1]) + vertices_homo = tf.concat([vertices, ones], axis=2) + skinned_homo = tf.matmul(T, tf.expand_dims(vertices_homo, -1)) + skinned_vertices = skinned_homo[:, :, :3, 0] + + return skinned_vertices diff --git a/src/smpl.py b/src/smpl.py new file mode 100644 index 0000000..a0c14b5 --- /dev/null +++ b/src/smpl.py @@ -0,0 +1,127 @@ +import pickle + +import numpy as np +import tensorflow as tf +import tensorflow.keras as keras + +from . import math +from . import skinning + + +class SMPL(keras.layers.Layer): + def __init__(self, model_path, name="smpl", **kwargs): + super(SMPL, self).__init__(name=name, **kwargs) + + with open(model_path, 'rb') as f: + dd = pickle.load(f, encoding='latin1') + + self.num_shapes = dd['shapedirs'].shape[-1] + self.num_vertices = dd["v_template"].shape[-2] + self.num_faces = dd["f"].shape[-2] + self.num_joints = dd["J_regressor"].shape[0] + + self.skinning_weights = tf.convert_to_tensor( + value=dd["weights"], + dtype=self.dtype, + name="skinning_weights" + ) + + self.template_vertices = tf.convert_to_tensor( + value=dd["v_template"], + dtype=self.dtype, + name="template_vertices" + ) + + self.faces = tf.convert_to_tensor( + value=dd["f"], + dtype=tf.int32, + name="faces" + ) + + self.shapedirs = tf.convert_to_tensor( + value=dd["shapedirs"].reshape([-1, self.num_shapes]).T, + dtype=self.dtype, + name="shapedirs" + ) + + self.posedirs = tf.convert_to_tensor( + value=dd["posedirs"].reshape([-1, dd['posedirs'].shape[-1]]).T, + dtype=self.dtype, + name="posedirs" + ) + + self.joint_regressor = tf.convert_to_tensor( + value=dd["J_regressor"].T.todense(), + dtype=self.dtype, + name="joint_regressor" + ) + + self.kintree_table = dd['kintree_table'][0].astype(np.int32) + + + def call(self, shape=None, pose=None, translation=None): + # Add shape blenshape + shape_blendshape = tf.reshape( + tensor=tf.matmul(shape, self.shapedirs), + shape=[-1, self.num_vertices, 3], + name="shape_blendshape" + ) + + vs = self.template_vertices + shape_blendshape + + if pose is None: + return vs, tf.zeros((self.num_joints, 4, 4)) + + # Compute local joint locations and rotations + pose = tf.reshape(pose, [-1, self.num_joints, 3]) + joint_rotations_local = math.AxisAngleToMatrix()(pose) + joint_locations_local = tf.stack( + values=[ + tf.matmul(vs[:, :, 0], self.joint_regressor), + tf.matmul(vs[:, :, 1], self.joint_regressor), + tf.matmul(vs[:, :, 2], self.joint_regressor) + ], + axis=2, + name="joint_locations_local" + ) + + # Add pose blenshape + pose_feature = tf.reshape( + tensor=joint_rotations_local[:, 1:, :, :] - tf.eye(3), + shape=[-1, 9 * (self.num_joints - 1)] + ) + + pose_blendshape = tf.reshape( + tensor=tf.matmul(pose_feature, self.posedirs), + shape=[-1, self.num_vertices, 3], + name="pose_blendshape" + ) + + vp = vs + pose_blendshape + + # Compute global joint transforms + joint_transforms, joint_locations = skinning.PoseSkeleton()( + joint_rotations_local, + joint_locations_local, + self.kintree_table + ) + + # Apply linear blend skinning + v = skinning.LBS()(vp, joint_transforms, self.skinning_weights) + + # Apply translation + if translation is not None: + v += translation[:, tf.newaxis, :] + + tensor_dict = { + "shape_blendshape": shape_blendshape, + "pose_blendshape": pose_blendshape, + "pose_feature": pose_feature, + "joint_transforms": joint_transforms, + "joint_locations": joint_locations, + "joint_locations_local": joint_locations_local, + "vertices_shaped": vs, + "vertices_posed": vp + } + + return v, tensor_dict \ No newline at end of file