Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured
  3D Assets
Authors: name: Weiyu Li, name: Xuanyang Zhang, name: Zheng Sun, name: Di Qi, name: Hao Li, name: Wei Cheng, name: Weiwei Cai, name: Shihao Wu, name: Jiarui Liu, name: Zihao Wang, name: Xiao Chen, name: Feipeng Tian, name: Jianxiong Pan, name: Zeming Li, name: Gang Yu, name: Xiangyu Zhang, name: Daxin Jiang, name: Ping Tan

While generative artificial intelligence has advanced significantly across
text, image, audio, and video domains, 3D generation remains comparatively
underdeveloped due to fundamental challenges such as data scarcity, algorithmic
limitations, and ecosystem fragmentation. To this end, we present Step1X-3D, an
open framework addressing these challenges through: (1) a rigorous data
curation pipeline processing >5M assets to create a 2M high-quality dataset
with standardized geometric and textural properties; (2) a two-stage 3D-native
architecture combining a hybrid VAE-DiT geometry generator with an
diffusion-based texture synthesis module; and (3) the full open-source release
of models, training code, and adaptation modules. For geometry generation, the
hybrid VAE-DiT component produces TSDF representations by employing
perceiver-based latent encoding with sharp edge sampling for detail
preservation. The diffusion-based texture synthesis module then ensures
cross-view consistency through geometric conditioning and latent-space
synchronization. Benchmark results demonstrate state-of-the-art performance
that exceeds existing open-source methods, while also achieving competitive
quality with proprietary solutions. Notably, the framework uniquely bridges the
2D and 3D generation paradigms by supporting direct transfer of 2D control
techniques~(e.g., LoRA) to 3D synthesis. By simultaneously advancing data
quality, algorithmic fidelity, and reproducibility, Step1X-3D aims to establish
new standards for open research in controllable 3D asset generation.