文本到 3D AI 生成的工作原理：Meta 3D Gen、OpenAI Shap-E 等

import numpy as np

def sphere_sdf(x, y, z, radius=1.0):
    return np.sqrt(x**2 + y**2 + z**2) - radius

# Evaluate SDF at a 3D point
point = [0.5, 0.5, 0.5]
distance = sphere_sdf(*point)
print(f"Distance to sphere surface: {distance}")

def diffusion_training_step(model, x_0, text_embedding):
# Sample a random timestep
t = torch.randint(0, num_timesteps, (1,))

# Add noise to the input
noise = torch.randn_like(x_0)
x_t = add_noise(x_0, noise, t)

# Predict the noise
predicted_noise = model(x_t, t, text_embedding)

# Compute loss
loss = F.mse_loss(noise, predicted_noise)

return loss

# Training loop
for batch in dataloader:
x_0, text = batch
text_embedding = encode_text(text)
loss = diffusion_training_step(model, x_0, text_embedding)
loss.backward()
optimizer.step()

import torch
import torch.nn.functional as F
import pytorch3d.renderer as pr

def render_mesh(vertices, faces, image_size=256):
    # Create a renderer
    renderer = pr.MeshRenderer(
        rasterizer=pr.MeshRasterizer(),
        shader=pr.SoftPhongShader()
    )
    
    # Set up camera
    cameras = pr.FoVPerspectiveCameras()
    
    # Render
    images = renderer(vertices, faces, cameras=cameras)
    
    return images

# Example usage
vertices = torch.rand(1, 100, 3)  # Random vertices
faces = torch.randint(0, 100, (1, 200, 3))  # Random faces
rendered_images = render_mesh(vertices, faces)

class TextTo3D(nn.Module):
    def __init__(self):
        super().__init__()
        self.text_encoder = BertModel.from_pretrained('bert-base-uncased')
        self.diffusion_model = DiffusionModel()
        self.refiner = RefinerNetwork()
        self.renderer = DifferentiableRenderer()
    
    def forward(self, text_prompt):
        # Encode text
        text_embedding = self.text_encoder(text_prompt).last_hidden_state.mean(dim=1)
        
        # Generate initial 3D representation
        initial_3d = self.diffusion_model(text_embedding)
        
        # Render multiple views
        views = self.renderer(initial_3d, num_views=4)
        
        # Refine based on multi-view consistency
        refined_3d = self.refiner(initial_3d, views)
        
        return refined_3d

# Usage
model = TextTo3D()
text_prompt = "A red sports car"
generated_3d = model(text_prompt)

顶级文本到 3D 资产模型可用

3DGen – 元

3DGen 旨在解决从文本描述生成 3D 内容（例如角色、道具和场景）的问题。

大型语言和文本到 3D 模型 – 3d-代

3DGen 支持基于物理的渲染 (PBR)，这对于在实际应用中实现逼真的 3D 资产重光照至关重要。它还支持使用新的文本输入对之前生成或艺术家创建的 3D 形状进行生成性重纹理。该管道集成了两个核心组件：Meta 3D AssetGen 和 Meta 3D TextureGen，分别处理文本到 3D 和文本到纹理的生成。

Meta 3D 资产生成器

Meta 3D AssetGen（Siddiqui 等人，2024 年）负责根据文本提示初始生成 3D 资源。此组件可在约 3 秒内生成带有纹理和 PBR 材质贴图的 30D 网格。

Meta 3D 纹理生成器

Meta 3D TextureGen（Bensadoun 等人，2024）细化 AssetGen 生成的纹理。它还可用于根据附加的文本描述为现有 3D 网格生成新纹理。此阶段大约需要 20 秒。