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

• URL: http://arxiv.org/abs/2604.00494v1
• Date: Wed, 01 Apr 2026 05:21:59 GMT
• Title: ARGS: Auto-Regressive Gaussian Splatting via Parallel Progressive Next-Scale Prediction
• Abstract summary: We introduce auto-regressive Gaussian splatting (ARGS), a framework for making next-scale predictions in parallel for generation according to levels of detail.<n>Benefiting from the use of hierarchical trees, the generation process requires only (mathcalO(log n)) steps, where (n) is the number of points.<n>Our approach effectively generates multi-scale Gaussian representations with controllable levels of detail, visual fidelity, and a manageable time consumption budget.
• Score: 24.251400178019328
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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