How to generate 778 points from 61 points

[HanYuYoung]

followed the code in this project and simplified it a bit. I would like to ask how these 61 points generate 778*3 points. I feel that the result is strange after generation. I hope you can take a look and tell me what needs to be modified.

import pickle
import numpy as np
import torch
from manopth.manolayer import ManoLayer
import os

# 2. 解析提供的数据
vertices = [
    2.2324724197387686,
    0.17883239686489108,
    -0.21358124911785112,
    0.05220034718513489,
    -0.16106925904750824,
    -0.07513493299484253,
    -0.024418428540229797,
    -0.003119782544672489,
    0.5173907279968262,
    0.1269320398569107,
    0.06313903629779816,
    0.41491588950157166,
    -0.12646515667438507,
    -0.13528624176979065,
    0.6040743589401245,
    -0.06075223162770271,
    -0.07627902179956436,
    0.4660395085811615,
    -0.008370521478354931,
    0.060622330754995346,
    0.3614122271537781,
    -0.6038511991500854,
    0.27024397253990173,
    0.960309624671936,
    -0.0069151027128100395,
    -0.0011883098632097244,
    0.3552946150302887,
    -0.4096725583076477,
    -0.03698532655835152,
    -0.012458777986466885,
    -0.13010376691818237,
    0.10553926974534988,
    0.9122609496116638,
    -0.2833951711654663,
    0.04361569136381149,
    0.2645259499549866,
    -0.15045560896396637,
    0.052607789635658264,
    0.31532639265060425,
    0.2744719088077545,
    -0.09708910435438156,
    -0.29489558935165405,
    -0.43372780084609985,
    -0.10651036351919174,
    0.21608810126781464,
    0.2551076114177704,
    -0.12822265923023224,
    -0.19144994020462036,
    -0.31784361600875854,
    -0.0012996774166822433,
    0.006854147184640169,
    0.0,
    0.0,
    0.0,
    0.0,
    0.0,
    0.0,
    0.0,
    88.70099920875388,
    107.33331026139358,
    661.7427649346813
]
    

# 提取shape、pose和camera参数
shape_params = np.array(vertices[48:58])
pose_params = np.array(vertices[:48])

# 3. 初始化 MANO 模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
mano_layer = ManoLayer(
    mano_root='./mano_models',
    use_pca=False,  # 使用完整的 48 维姿态参数
    flat_hand_mean=False
).to(device)

# 4. 将参数转换为张量
shape_tensor = torch.tensor(shape_params, dtype=torch.float32).unsqueeze(0).to(device)
pose_tensor = torch.tensor(pose_params, dtype=torch.float32).unsqueeze(0).to(device)

# 5. 通过MANO模型获取手部网格
hand_verts, hand_joints = mano_layer(pose_tensor, shape_tensor)

# 6. 应用相机参数
# FreiHAND相机模型: [焦距, 主点x, 主点y]

# 获取顶点并转到CPU
vertices = hand_verts.squeeze().detach().cpu().numpy()/1000.0  # [778, 3]

# 7. 保存为OBJ文件
def save_obj(vertices, faces, filepath):
    with open(filepath, 'w') as fp:
        for v in vertices:
            fp.write('v %f %f %f\n' % (v[0], v[1], v[2]))
        
        for f in faces:
            # OBJ文件索引从1开始
            fp.write('f %d %d %d\n' % (f[0] + 1, f[1] + 1, f[2] + 1))
    print(f"保存了包含{len(vertices)}个顶点的手部模型到{filepath}")

# 获取面片信息
faces = mano_layer.th_faces.detach().cpu().numpy()

# 保存OBJ文件
output_path = "hand_model.obj"
save_obj(vertices, faces, output_path)

# 8. 验证顶点数量
print(f"生成的手部模型有{len(vertices)}个顶点")