AI 3D 内容生成：从文本到 3D 模型

from meshy import MeshyClient

client = MeshyClient(api_key="your_api_key")

# 文本转 3D
model = client.create_model_from_text(
    prompt="A medieval fantasy sword with ornate golden hilt, "
           "engraved blade, leather wrapped handle, highly detailed",
    mode="quality",  # quality / fast
    art_style="realistic",  # realistic / stylized / cartoon
    seed=42
)

# 等待生成完成
model.wait_for_completion()

# 下载模型
model.download("sword.obj", format="obj")
model.download("sword.glb", format="glb")

✅ 好的 prompt：
"A weathered wooden treasure chest with iron reinforcements, 
ornate lock mechanism, visible wood grain texture, 
slightly open with golden light emanating from inside, 
medieval fantasy style"

❌ 不好的 prompt：
"a chest"

# 生成 PBR 纹理（包含法线、粗糙度、金属度贴图）
model = client.create_model_from_text(
    prompt="...",
    enable_pbr=True,
    texture_resolution=2048  # 1024 / 2048 / 4096
)

# PBR 纹理包含：
# - base_color.png  基础色
# - normal.png      法线贴图
# - roughness.png   粗糙度
# - metallic.png    金属度
# - ao.png          环境光遮蔽

from trip import Tripo3DClient

client = Tripo3DClient(api_key="your_api_key")

# 图片转 3D
model = client.image_to_3d(
    image_path="product_photo.jpg",
    quality="high",
    output_format="glb"
)

model.wait_for_completion()
model.download("product_3d.glb")

# 图片预处理提升 3D 质量
def preprocess_image_for_3d(image_path):
    """图片预处理"""
    
    # 1. 背景移除（SAM 2）
    from segment_anything import SamPredictor
    predictor = SamPredictor()
    foreground = predictor.remove_background(image_path)
    
    # 2. 居中裁剪
    centered = center_object(foreground)
    
    # 3. 分辨率提升
    upscaled = super_resolve(centered, target_size=1024)
    
    # 4. 亮度归一化
    normalized = normalize_brightness(upscaled)
    
    return normalized

from luma import LumaClient

client = LumaClient(api_key="your_api_key")

# 方式一：视频转 3D
capture = client.create_capture_from_video(
    video_path="room_scan.mp4",
    mode="gaussian_splat",  # gaussian_splat / nerf / mesh
    quality="high"
)

# 方式二：多图转 3D
capture = client.create_capture_from_photos(
    photo_paths=["photo1.jpg", "photo2.jpg", "photo3.jpg", 
                 "photo4.jpg", "photo5.jpg"],
    mode="gaussian_splat"
)

# 等待处理
capture.wait_for_completion()

# 导出
capture.export("room.glb", format="glb")
capture.export("room.ply", format="ply")  # 原始高斯点云
capture.export("room.usdz", format="usdz")  # Apple AR

3D 扫描拍摄指南：

1. 围绕物体缓慢移动（30-60秒视频）
2. 保持 60-70% 画面重叠
3. 覆盖所有角度（正面、侧面、顶部、底部）
4. 均匀光照（避免强烈阴影）
5. 避免透明/反光物体
6. 保持稳定（使用稳定器或慢走）
7. 距离一致（保持相同距离环绕）

# 安装
# pip install nerfstudio

# 训练 NeRF
# ns-train nerfacto --data path/to/images

# 导出
# ns-export gaussian-splat --load-config path/to/config.yml

# 批量生成游戏道具
game_props = [
    "wooden barrel with metal bands",
    "iron sword with leather grip",
    "crystal potion bottle with cork stopper",
    "wooden shield with iron boss",
    "torch with flickering flame"
]

models = []
for prop in game_props:
    model = await meshy.create_model_from_text(
        prompt=prop,
        mode="fast",  # 游戏道具用快速模式
        art_style="stylized"
    )
    models.append(model)

# 产品图转 3D 模型（用于 AR 试穿/试用）
async def create_3d_product(image_path, category):
    # 预处理图片
    processed = preprocess_image_for_3d(image_path)
    
    # 生成 3D
    model = await tripo.image_to_3d(
        image=processed,
        quality="high"
    )
    
    # 生成 USDZ（用于 iOS AR）
    model.export("product.usdz")
    
    # 生成 GLB（用于 Web AR）
    model.export("product.glb")
    
    return model

# 建筑渲染图转 3D
model = await meshy.create_model_from_text(
    prompt="modern minimalist house, glass facade, "
           "flat roof, concrete and wood materials, "
           "surrounded by trees, architectural visualization",
    mode="quality",
    art_style="realistic"
)

# 实景扫描 → VR 场景
capture = await luma.create_capture_from_video(
    video_path="store_walkthrough.mp4",
    mode="gaussian_splat"
)

# 导出为 VR 格式
capture.export("store_vr.usdz")  # Apple Vision Pro
capture.export("store_vr.glb")   # WebXR

import trimesh

class ModelPostProcessor:
    """3D 模型后处理"""
    
    def process(self, mesh_path):
        """自动修复和优化"""
        mesh = trimesh.load(mesh_path)
        
        # 1. 修复非流形几何
        mesh = self._fix_non_manifold(mesh)
        
        # 2. 填补孔洞
        mesh = self._fill_holes(mesh)
        
        # 3. 简化（控制面数）
        mesh = self._simplify(mesh, target_faces=10000)
        
        # 4. UV 展开
        mesh = self._unwrap_uv(mesh)
        
        # 5. 法线平滑
        mesh = self._smooth_normals(mesh)
        
        return mesh
    
    def _simplify(self, mesh, target_faces):
        """网格简化"""
        if len(mesh.faces) > target_faces:
            mesh = mesh.simplify_quadric_decimation(target_faces)
        return mesh

import bpy

def auto_optimize_model(input_path, output_path):
    """使用 Blender 自动优化"""
    # 清空场景
    bpy.ops.object.select_all(action='SELECT')
    bpy.ops.object.delete()
    
    # 导入模型
    bpy.ops.import_scene.obj(filepath=input_path)
    
    # 自动平滑法线
    bpy.ops.object.shade_smooth()
    
    # 添加修改器
    obj = bpy.context.active_object
    mod = obj.modifiers.new("Decimate", 'DECIMATE')
    mod.ratio = 0.5  # 减面 50%
    
    # 烘焙纹理
    bpy.ops.object.bake(type='DIFFUSE')
    
    # 导出
    bpy.ops.export_scene.gltf(filepath=output_path)