百度数字人平台实战指南

import numpy as np # ============================================================# 百度数字人核心技术模拟：面部关键点动画与表情融合# 基于 NumPy 实现 68 点面部关键点模型、BlendShape 插值# 和唇形同步动画时序# ============================================================ # 1. 定义 68 点面部关键点（简化模型：仅展示关键区域）# 真实场景中关键点来自 MediaPipe 或 dlib，此处用模拟数据np.random.seed(42)FACIAL_LANDMARKS = np.random.randn(68, 3) * 0.1  # 68 点 x (x, y, z) # 2. 定义 BlendShape 基础表情（blendshape basis）# 6 种基础表情：中性、微笑、惊讶、愤怒、悲伤、嘟嘴blendshapes = {    "neutral":   np.zeros((68, 3)),    "smile":     np.random.randn(68, 3) * 0.05,    "surprise":  np.random.randn(68, 3) * 0.08,    "angry":     np.random.randn(68, 3) * 0.06,    "sad":       np.random.randn(68, 3) * 0.04,    "pout":      np.random.randn(68, 3) * 0.05,} # 3. BlendShape 线性插值：混合多种表情def blend_expressions(weights_dict):    """按权重融合多个 BlendShape，生成复合表情。    weights_dict: {"smile": 0.6, "surprise": 0.3, "neutral": 0.1}    返回: 融合后的 68 点关键点坐标 (68, 3)    """    result = np.zeros((68, 3))    total_weight = 0.0    for name, weight in weights_dict.items():        if name in blendshapes:            result += blendshapes[name] * weight            total_weight += weight    if total_weight > 0:        result /= total_weight    return result + FACIAL_LANDMARKS # 4. 唇形同步动画时序：viseme 序列# viseme: 音素对应的口型，如 "AA", "E", "IH", "O", "U", "M", "F", "SIL"viseme_sequence = ["AA", "IH", "O", "U", "E", "SIL", "M", "F", "AA", "O"]viseme_duration = [0.15, 0.12, 0.18, 0.10, 0.14, 0.20, 0.13, 0.11, 0.16, 0.12] # 每个 viseme 对应一个口型权重（控制嘴唇关键点偏移）viseme_mouth_shapes = {    "AA":  {"pout": 0.1, "smile": 0.2},    "IH":  {"pout": 0.3, "smile": 0.5},    "O":   {"pout": 0.8, "smile": 0.0},    "U":   {"pout": 0.9, "smile": 0.0},    "E":   {"pout": 0.0, "smile": 0.7},    "SIL": {"pout": 0.0, "smile": 0.0},    "M":   {"pout": 0.6, "smile": 0.1},    "F":   {"pout": 0.0, "smile": 0.1},} def generate_lip_sync_frames(viseme_seq, duration_seq, fps=30):    """根据 viseme 序列和时长生成唇形同步动画帧。    返回: frames 列表，每帧为 (68, 3) 的面部关键点    """    frames = []    for viseme, dur in zip(viseme_seq, duration_seq):        num_frames = max(1, int(dur * fps))        shape = viseme_mouth_shapes.get(viseme, {"pout": 0.0, "smile": 0.0})        for _ in range(num_frames):            frame = blend_expressions(shape)            frames.append(frame)    return frames # 5. 姿态平滑插值：实现数字人头部/身体的平滑过渡def slerp_pose(pose_a, pose_b, num_steps=10):    """在两姿态间进行球面线性插值（简化版），生成平滑过渡序列。    pose_a, pose_b: (3,) 欧拉角 (roll, pitch, yaw)    """    t_values = np.linspace(0, 1, num_steps)    poses = []    for t in t_values:        interp = (1 - t) * pose_a + t * pose_b        # 归一化角度（模拟）        interp = np.mod(interp + np.pi, 2 * np.pi) - np.pi        poses.append(interp)    return np.array(poses) # 6. 运行演示if False and __name__ == "__main__":    print("=== 百度数字人核心技术 NumPy 模拟 ===\n")     # 演示 BlendShape 融合    expr = blend_expressions({"smile": 0.7, "surprise": 0.3})    print(f"融合表情关键点均值: {expr.mean():.4f}")     # 演示唇形同步    frames = generate_lip_sync_frames(viseme_sequence, viseme_duration, fps=30)    print(f"唇形同步动画帧数: {len(frames)}")     # 演示姿态插值    pose_start = np.array([0.0, 0.0, 0.0])    pose_end = np.array([0.3, -0.1, 0.5])    smooth_poses = slerp_pose(pose_start, pose_end, num_steps=10)    print(f"姿态插值步数: {len(smooth_poses)}")    print(f"起始姿态: {smooth_poses[0]}")    print(f"结束姿态: {smooth_poses[-1]}")    print("\n=== 模拟完成 ===")