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import smplx
import torch
import pickle
import numpy as np
import os
import trimesh
import cv2
os.environ['PYOPENGL_PLATFORM'] = 'egl'
import pyrender
root_dir = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../..'))
def get_smplx_model(bs, deivce):
smpl_model = smplx.create(os.path.join(root_dir, 'deps/smplx/models'),
model_type='smplx',
gender='male',
ext='npz',
batch_size=bs,
).to(device)
return smpl_model.eval()
def render_motion_to_video(motion, device, output_path, title, step=2):
length = motion['transl'].shape[0]
model = get_smplx_model(length, device)
vertices = model(
body_pose = torch.tensor(motion['body_pose'], dtype=torch.float32).to(device),
transl = torch.tensor(motion['transl'], dtype=torch.float32).to(device),
global_orient = torch.tensor(motion['global_orient'], dtype=torch.float32).to(device)
).vertices.detach().cpu().numpy()
faces = model.faces
temp_dir = "temp_render_images"
os.makedirs(temp_dir, exist_ok=True)
width, height = 480, 320
images = []
for i in range(0, length, step):
trimesh_mesh = trimesh.Trimesh(vertices=vertices[i], faces=faces)
mesh = pyrender.Mesh.from_trimesh(trimesh_mesh)
scene = pyrender.Scene(ambient_light=[0.5, 0.5, 0.5])
scene.add(mesh)
camera = pyrender.PerspectiveCamera(yfov=np.pi / 3.0)
angle = -np.pi / 8
camera_pose = np.array([
[1, 0, 0, 0],
[0, np.cos(angle), -np.sin(angle), 2.0],
[0, np.sin(angle), np.cos(angle), 4.0],
[0, 0, 0, 1]
])
scene.add(camera, pose=camera_pose)
light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=2.0)
scene.add(light, pose=camera_pose)
r = pyrender.OffscreenRenderer(width, height)
color, _ = r.render(scene)
r.delete()
image = cv2.cvtColor(color, cv2.COLOR_RGB2BGR)
img_path = os.path.join(temp_dir, f"frame_{i:04d}.png")
cv2.imwrite(img_path, image)
images.append(image)
if images:
height, width, _ = images[0].shape
sampled_fps = int(20 / step)
temp_video_path = output_path.replace('.mp4', '_temp.mp4')
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video = cv2.VideoWriter(temp_video_path, fourcc, sampled_fps, (width, height))
for image in images:
video.write(image)
video.release()
try:
import subprocess
cmd = [
'ffmpeg',
'-y',
'-i', temp_video_path,
'-filter:v', f'minterpolate=fps={24}:mi_mode=mci:mc_mode=aobmc:me_mode=bidir:vsbmc=1',
'-c:v', 'libx264',
'-preset', 'medium',
'-crf', '18',
output_path
]
subprocess.run(cmd, check=True)
print(f"帧插值完成，最终视频保存至: {output_path}")
os.remove(temp_video_path)
except Exception as e:
print(f"帧插值失败: {e}")
os.rename(temp_video_path, output_path)
print(f"使用原始视频: {output_path}")
else:
print("没有图像可用于创建视频")
for file in os.listdir(temp_dir):
os.remove(os.path.join(temp_dir, file))
os.rmdir(temp_dir)
from argparse import ArgumentParser
import time
if __name__ == "__main__":
parser = ArgumentParser()
print('******Start rendering...******')
start_time = time.time()
parser.add_argument("--motion_path", type=str, required=True)
parser.add_argument("--title", type=str, default="rendered_motion.mp4")
args = parser.parse_args()
with open(args.motion_path, 'rb') as f:
motion = pickle.load(f)
output_path = args.motion_path.replace(".pkl", ".mp4")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(args.title)
render_motion_to_video(motion, device, output_path, args.title)
print(f"******Rendering finished in {time.time() - start_time:.2f} seconds.******")
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