论文标题
3D-LDM：具有潜扩散模型的神经隐式3D形状生成
3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models
论文作者
论文摘要
扩散模型对图像产生显示了巨大的希望，在产生多样性方面击败了gan，图像质量可比。但是，它们在3D形状上的应用仅限于点或体素表示，实际上可以准确地代表3D表面。我们提出了一个在自动描述器潜在空间中运行的3D形状神经隐式表示的扩散模型。这使我们能够产生多样化和高质量的3D表面。我们还表明，我们可以在图像或文本上调节我们的模型，以启用使用夹嵌入的图像到3D生成和文本对3D的生成。此外，在现有形状的潜在代码中添加噪声使我们能够探索形状变化。
Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shapes has been limited to point or voxel representations that can in practice not accurately represent a 3D surface. We propose a diffusion model for neural implicit representations of 3D shapes that operates in the latent space of an auto-decoder. This allows us to generate diverse and high quality 3D surfaces. We additionally show that we can condition our model on images or text to enable image-to-3D generation and text-to-3D generation using CLIP embeddings. Furthermore, adding noise to the latent codes of existing shapes allows us to explore shape variations.
