  Generating high-quality 3D assets from text and images has long been
challenging, primarily due to the absence of scalable 3D representations
capable of capturing intricate geometry distributions. In this work, we
introduce Direct3D, a native 3D generative model scalable to in-the-wild input
images, without requiring a multiview diffusion model or SDS optimization. Our
approach comprises two primary components: a Direct 3D Variational Auto-Encoder
(D3D-VAE) and a Direct 3D Diffusion Transformer (D3D-DiT). D3D-VAE efficiently
encodes high-resolution 3D shapes into a compact and continuous latent triplane
space. Notably, our method directly supervises the decoded geometry using a
semi-continuous surface sampling strategy, diverging from previous methods
relying on rendered images as supervision signals. D3D-DiT models the
distribution of encoded 3D latents and is specifically designed to fuse
positional information from the three feature maps of the triplane latent,
enabling a native 3D generative model scalable to large-scale 3D datasets.
Additionally, we introduce an innovative image-to-3D generation pipeline
incorporating semantic and pixel-level image conditions, allowing the model to
produce 3D shapes consistent with the provided conditional image input.
Extensive experiments demonstrate the superiority of our large-scale
pre-trained Direct3D over previous image-to-3D approaches, achieving
significantly better generation quality and generalization ability, thus
establishing a new state-of-the-art for 3D content creation. Project page:
https://nju-3dv.github.io/projects/Direct3D/.
