ZERO-1-to-G: Taming Pretrained 2D Diffusion Models for Direct 3D Generation
Keywords: native 3D diffusion, 3D generative model, gaussian splats
TL;DR: We present Zero-1-to-G, a novel native 3D diffusion model for Gaussian splats using multiview splatter images.
Abstract: Recent advances in 2D image generation have achieved remarkable quality, largely driven by the capacity of diffusion models and the availability of large-scale datasets. However, direct 3D generation is still constrained by the scarcity and lower fidelity of 3D datasets. In this paper, we introduce Zero-1-to-G, a novel approach that addresses this problem by enabling direct 3D generation on Gaussian splats through 2D diffusion models. Our key insight is that Gaussian splats, a 3D representation, can be decomposed into multi-view images encoding different attributes. This reframes the challenging task of direct 3D generation within a 2D diffusion framework, allowing us to leverage the rich priors of pretrained 2D diffusion models. To incorporate 3D awareness, we introduce cross-view and cross-attribute attention layers, which capture complex correlations and enforce 3D consistency across generated splats. This makes Zero-1-to-G the first direct 3D generative model to effectively utilize 2D pretrained diffusion priors, enabling efficient training and improved generalization to unseen objects. Extensive experiments on both synthetic and in-the-wild datasets demonstrate superior performance in 3D object generation, offering a new approach to high-quality 3D generation.
Primary Area: generative models
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Submission Number: 1229
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