Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse
  Attention
Authors: name: Shuang Wu, name: Youtian Lin, name: Feihu Zhang, name: Yifei Zeng, name: Yikang Yang, name: Yajie Bao, name: Jiachen Qian, name: Siyu Zhu, name: Philip Torr, name: Xun Cao, name: Yao Yao

Generating high resolution 3D shapes using volumetric representations such as
Signed Distance Functions presents substantial computational and memory
challenges. We introduce Direct3D S2, a scalable 3D generation framework based
on sparse volumes that achieves superior output quality with dramatically
reduced training costs. Our key innovation is the Spatial Sparse Attention
mechanism, which greatly enhances the efficiency of Diffusion Transformer
computations on sparse volumetric data. SSA allows the model to effectively
process large token sets within sparse volumes, significantly reducing
computational overhead and achieving a 3.9x speedup in the forward pass and a
9.6x speedup in the backward pass. Our framework also includes a variational
autoencoder that maintains a consistent sparse volumetric format across input,
latent, and output stages. Compared to previous methods with heterogeneous
representations in 3D VAE, this unified design significantly improves training
efficiency and stability. Our model is trained on public available datasets,
and experiments demonstrate that Direct3D S2 not only surpasses
state-of-the-art methods in generation quality and efficiency, but also enables
training at 1024 resolution using only 8 GPUs, a task typically requiring at
least 32 GPUs for volumetric representations at 256 resolution, thus making
gigascale 3D generation both practical and accessible. Project page:
https://nju3dv.github.io/projects/Direct3D-S2/.