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arXiv · 2604.00494

ARGS: Auto-Regressive Gaussian Splatting via Parallel Progressive Next-Scale Prediction

Quanyuan Ruan·Kewei Shi·Jiabao Lei·Xifeng Gao·Xiaoguang Han
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Abstract

Auto-regressive frameworks for next-scale prediction of 2D images have demonstrated strong potential for producing diverse and sophisticated content by progressively refining a coarse input. However, extending this paradigm to 3D object generation remains largely unexplored. In this paper, we introduce auto-regressive Gaussian splatting (ARGS), a framework for making next-scale predictions in parallel for generation according to levels of detail. We propose a Gaussian simplification strategy and reverse the simplification to guide next-scale generation. Benefiting from the use of hierarchical trees, the generation process requires only \(\mathcal{O}(\log n)\) steps, where \(n\) is the number of points. Furthermore, we propose a tree-based transformer to predict the tree structure auto-regressively, allowing leaf nodes to attend to their internal ancestors to enhance structural consistency. Extensive experiments demonstrate that our approach effectively generates multi-scale Gaussian representations with controllable levels of detail, visual fidelity, and a manageable time consumption budget.

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Quanyuan Ruan, Kewei Shi, Jiabao Lei, Xifeng Gao, Xiaoguang Han. 2026-04-01. ARGS: Auto-Regressive Gaussian Splatting via Parallel Progressive Next-Scale Prediction. https://arxiv.org/abs/2604.00494

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