FlashWorld: High-quality 3D Scene Generation within Seconds
Authors: name: Xinyang Li, name: Tengfei Wang, name: Zixiao Gu, name: Shengchuan Zhang, name: Chunchao Guo, name: Liujuan Cao

We propose FlashWorld, a generative model that produces 3D scenes from a
single image or text prompt in seconds, 10~100times faster than previous
works while possessing superior rendering quality. Our approach shifts from the
conventional multi-view-oriented (MV-oriented) paradigm, which generates
multi-view images for subsequent 3D reconstruction, to a 3D-oriented approach
where the model directly produces 3D Gaussian representations during multi-view
generation. While ensuring 3D consistency, 3D-oriented method typically suffers
poor visual quality. FlashWorld includes a dual-mode pre-training phase
followed by a cross-mode post-training phase, effectively integrating the
strengths of both paradigms. Specifically, leveraging the prior from a video
diffusion model, we first pre-train a dual-mode multi-view diffusion model,
which jointly supports MV-oriented and 3D-oriented generation modes. To bridge
the quality gap in 3D-oriented generation, we further propose a cross-mode
post-training distillation by matching distribution from consistent 3D-oriented
mode to high-quality MV-oriented mode. This not only enhances visual quality
while maintaining 3D consistency, but also reduces the required denoising steps
for inference. Also, we propose a strategy to leverage massive single-view
images and text prompts during this process to enhance the model's
generalization to out-of-distribution inputs. Extensive experiments demonstrate
the superiority and efficiency of our method.