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

import numpy as np

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

<p># Evaluate SDF at a 3D point
point = [0.5, 0.5, 0.5]
distance = sphere_sdf(*point)
print(f&quot;Distance to sphere surface: {distance}&quot;)

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

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

<p># Predict the noise
predicted_noise = model(x_t, t, text_embedding)</p>

<p># Compute loss
loss = F.mse_loss(noise, predicted_noise)</p>

return loss

<p># 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

<p>def render_mesh(vertices, faces, image_size=256):
# Create a renderer
renderer = pr.MeshRenderer(
rasterizer=pr.MeshRasterizer(),
shader=pr.SoftPhongShader()
)</p>

<p># Set up camera
cameras = pr.FoVPerspectiveCameras()</p>

<p># Render
images = renderer(vertices, faces, cameras=cameras)</p>

return images

<p># 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()

<p>def forward(self, text_prompt):
# Encode text
text_embedding = self.text_encoder(text_prompt).last_hidden_state.mean(dim=1)</p>

<p># Generate initial 3D representation
initial_3d = self.diffusion_model(text_embedding)</p>

<p># Render multiple views
views = self.renderer(initial_3d, num_views=4)</p>

<p># Refine based on multi-view consistency
refined_3d = self.refiner(initial_3d, views)</p>

return refined_3d

<p># Usage
model = TextTo3D()
text_prompt = &quot;A red sports car&quot;
generated_3d = model(text_prompt)

可用的顶级文本到3D资产模型

3DGen – Meta

3DGen旨在解决从文本描述生成3D内容（如角色、道具和场景）的问题。

Large Language and Text-to-3D Models – 3d-gen

3DGen支持基于物理的渲染（PBR），这对于在现实世界应用中对3D资产进行重新照明是必不可少的。它还可以使用新的文本输入对以前生成或艺术家创建的3D形状进行生成性重新纹理。该管道集成了两个核心组件：Meta 3D AssetGen和Meta 3D TextureGen，它们分别处理文本到3D和文本到纹理的生成。

Meta 3D AssetGen

Meta 3D AssetGen（Siddiqui et al.，2024）负责从文本提示生成3D资产的初始生成。该组件生成一个带有纹理和PBR材质映射的3D网格，耗时约30秒。

Meta 3D TextureGen

Meta 3D TextureGen（Bensadoun et al.，2024）细化了AssetGen生成的纹理。它还可以根据额外的文本描述为现有的3D网格生成新的纹理。该阶段耗时约20秒。