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# SPDX-License-Identifier: Apache-2.0
"""
MHR shape to SMPL shape converter.
This class handles the conversion of MHR shape parameters to SMPL shape parameters.
"""
import argparse
from pathlib import Path
import imageio
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import trimesh
from tqdm import tqdm
from soma.geometry.barycentric_interp import BarycentricInterpolator
from soma.geometry.batched_skinning import BatchedSkinning
from soma.soma import SOMALayer
from tools.vis_pyrender import MeshRenderer, look_at, set_pyopengl_platform
def get_smooth_noise(T, dim, device, num_keyframes=None, mode="normal"):
if num_keyframes is None:
num_keyframes = max(3, T // 30)
if mode == "normal":
keyframes = torch.randn(1, dim, num_keyframes, device=device)
elif mode == "uniform":
keyframes = torch.rand(1, dim, num_keyframes, device=device)
res = F.interpolate(keyframes, size=T, mode="linear", align_corners=True)[0].T
return res
class ShapeTransfer(nn.Module):
"""
MHR shape to SMPL shape converter.
"""
def __init__(self, data_root, device="cuda"):
"""
Initialize the shape transfer.
"""
super().__init__()
self.data_root = Path(data_root)
self.device = device
self.mhr_soma = SOMALayer(
data_root=data_root, device=device, identity_model_type="mhr", mode="warp"
)
self.smpl_soma = SOMALayer(
data_root=data_root, device=device, identity_model_type="smpl", mode="warp"
)
self.soma_to_smpl = self.get_soma_to_smpl_interpolator()
smpl_rest_mesh = trimesh.load(
self.data_root / "SMPL" / "smpl_base_body.obj", maintain_order=True, process=False
)
smpl_rest_shape = torch.from_numpy(smpl_rest_mesh.vertices).float().to(device).unsqueeze(0)
self.smpl_rest_shape_soma = self.smpl_soma.identity_model.identity_model_to_soma(
smpl_rest_shape
)
self.posed_world_smpl_tpose = self.mhr_soma.skeleton_transfer.fit(self.smpl_rest_shape_soma)
def get_soma_to_smpl_interpolator(self):
mesh_smpl = trimesh.load(
self.data_root / "SMPL" / "smpl_base_body.obj", maintain_order=True, process=False
)
V_smpl = torch.from_numpy(mesh_smpl.vertices).float().to(self.device)
mesh_nv = trimesh.load(
self.data_root / "SMPL" / "Nova_wrap.obj", maintain_order=True, process=False
)
V_nv = torch.from_numpy(mesh_nv.vertices).float().to(self.device)
F_nv = torch.from_numpy(mesh_nv.faces).int().to(self.device)
return BarycentricInterpolator(V_nv, F_nv, V_smpl)
def forward(self, identity_coeffs, scale_params):
"""
Forward pass.
"""
batch_size = identity_coeffs.shape[0]
device = identity_coeffs.device
# 1. Get MHR rest shape
mhr_rest_shape_soma = self.mhr_soma.identity_model(identity_coeffs, scale_params)
# 2. Get MHR posed world transforms
posed_world_mhr = self.mhr_soma.skeleton_transfer.fit(mhr_rest_shape_soma)
# 3. Skin the MHR rest shape to get it under the SMPL rest pose
batched_skinning = BatchedSkinning(
self.mhr_soma.joint_parent_ids,
self.mhr_soma.skinning_weights,
posed_world_mhr,
mhr_rest_shape_soma,
joint_orient=self.posed_world_smpl_tpose[0],
mode=self.mhr_soma.mode,
)
pose_rotations = torch.eye(3, device=device).unsqueeze(0).expand(batch_size, 78, 3, 3)
pose_translations = self.posed_world_smpl_tpose[:, 1, :3, 3].expand(batch_size, 3)
vertices, T_world = batched_skinning.pose(
local_rotations=pose_rotations,
hips_translations=pose_translations,
return_transforms=True,
)
# 4. Get the SMPL topology vertices
mhr_vertices_smpl = self.soma_to_smpl(vertices)
# 5. Solve the betas
B = (
mhr_vertices_smpl - self.smpl_soma.identity_model.identity_model.v_template[None]
).reshape(batch_size, -1)
A = self.smpl_soma.identity_model.identity_model.shape_dirs.reshape(-1, 10)
betas = torch.linalg.lstsq(A, B.T).solution.T
return betas
# Example usage
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Shape transfer.")
parser.add_argument("--data_root", type=str, default="./assets", help="Path to the data root.")
parser.add_argument("--device", default="cuda:0")
parser.add_argument("--output-dir", default="out/")
parser.add_argument("--image-size", type=int, default=1920)
parser.add_argument("--sequence-length", type=int, default=300)
parser.add_argument("--pyopengl-platform", default="osmesa")
args = parser.parse_args()
set_pyopengl_platform(args.pyopengl_platform)
device = "cuda"
shape_transfer = ShapeTransfer(args.data_root, device)
T = args.sequence_length
mhr_im = shape_transfer.mhr_soma.identity_model
identity_coeffs = get_smooth_noise(T, mhr_im.num_identity_coeffs, device)
scale_params = get_smooth_noise(T, mhr_im.num_scale_params, device, mode="normal") * 0.2
zero_pose = torch.zeros(1, 77, 3, device=device)
zero_transl = torch.zeros(1, 3, device=device)
betas = shape_transfer(identity_coeffs, scale_params)
smpl_vertices = shape_transfer.smpl_soma(zero_pose, betas, None, zero_transl)["vertices"]
mhr_vertices = shape_transfer.mhr_soma(zero_pose, identity_coeffs, scale_params, zero_transl)[
"vertices"
]
smpl_vertices = smpl_vertices.detach().cpu().numpy()
mhr_vertices = mhr_vertices.detach().cpu().numpy()
faces = shape_transfer.mhr_soma.faces.cpu().numpy()
print("Rendering videos...")
colors = {
"mhr": (0.98, 0.65, 0.15, 1.0),
"anny": (0.25, 0.75, 1.0, 1.0),
"smpl": (0.55, 0.15, 0.85, 1.0),
}
def save_video(frames, path, fps=30):
imageio.mimsave(path, frames, fps=fps)
print(f"Saved {path}")
renderer = MeshRenderer(image_size=args.image_size, light_intensity=5)
cam_pose = look_at(
eye=np.array([0.0, 0.0, 6.0]),
target=np.array([0.0, 0.0, 0.0]),
up=np.array([0.0, 1.0, 0.0]),
)
light_dir = np.array([0.0, -0.5, -1.0])
faces = shape_transfer.mhr_soma.faces.cpu().numpy()
frames = []
for t in tqdm(range(T)):
mhr_img = renderer.render(
mhr_vertices[t],
faces,
mesh_color=colors["mhr"],
cam_pose=cam_pose,
light_dir=light_dir,
metallic=0.0,
roughness=0.5,
base_color_factor=[0.9, 0.9, 0.9, 1.0],
)
smpl_img = renderer.render(
smpl_vertices[t],
faces,
mesh_color=colors["smpl"],
cam_pose=cam_pose,
light_dir=light_dir,
metallic=0.0,
roughness=0.5,
base_color_factor=[0.9, 0.9, 0.9, 1.0],
)
merged_img = (0.5 * mhr_img + 0.5 * smpl_img).astype(np.uint8)
img = np.concatenate([mhr_img, merged_img, smpl_img], axis=1)
frames.append(img[..., ::-1])
renderer.delete()
save_video(frames, Path(args.output_dir) / "shape_transfer.mp4")
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