Direct3D S2

--- license: mit tags: - image-to-3d - 3d-aigc - 3d-reconstruction - 3d-models - 3d-generation --- # Direct3D‑S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention <div align="center"> <a href=https://www.neural4d.com/research/direct3d-s2 target="_blank"><img src=https://img.shields.io/badge/Project%20Page-333399.svg?logo=googlehome height=22px></a> <a href=https://huggingface.co/spaces/wushuang98/Direct3D-S2-v1.0-demo target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a> <a href=https://huggingface.co/spaces/wushuang98/Direct3D-S2-v1.0-demo target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a> <a href=https://arxiv.org/pdf/2505.17412 target="_blank"><img src=https://img.shields.io/badge/Arxiv-b5212f.svg?logo=arxiv height=22px></a> </div> <div style="background: #fff; box-shadow: 0 4px 12px rgba(0,0,0,.15); display: inline-block; padding: 0px;"> <img id="teaser" src="assets/teaserv6.png" alt="Teaser image of Direct3D-S2"/> </div> --- ## ✨ News - May 30, 2025: 🤯 We have released both v1.0 and v1.1. The new model offers even greater speed compared to FlashAttention-2, with **12.2×** faster forward pass and **19.7×** faster backward pass, resulting in nearly **2×** inference speedup over v1.0. - May 30, 2025: 🔨 Release inference code and model. - May 26, 2025: 🎁 Release live demo on 🤗 [Hugging Face](https://huggingface.co/spaces/wushuang98/Direct3D-S2-v1.0-demo). - May 26, 2025: 🚀 Release paper and project page. ## 📝 Abstract Generating high-resolution 3D shapes using volumetric representations such as Signed Distance Functions (SDFs) presents substantial computational and memory challenges. We introduce <strong class="has-text-weight-bold">Direct3D‑S2</strong>, a scalable 3D generation framework based on sparse volumes that achieves superior output quality with dramatically reduced training costs. Our key innovation is the <strong class="has-text-weight-bold">Spatial Sparse Attention (SSA)</strong> mechanism, which greatly enhances the efficiency of Diffusion Transformer (DiT) computations on sparse volumetric data. SSA allows the model to effectively process large token sets within sparse volumes, substantially reducing computational overhead and achieving a <em>3.9&times;</em> speedup in the forward pass and a <em>9.6&times;</...