FLUX3D: High-Fidelity 3D Gaussian Generation with Diffusion-Aligned Sparse Representation
Summary
Sparse voxel representation has emerged as a scalable foundation for image-to-3D Gaussian Splatting (3DGS) generation, yet current methods struggle to preserve high-frequency visual details of input images due to two structural bottlenecks.
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2606.24874v1 (cs)
[Submitted on 23 Jun 2026]
Title:FLUX3D: High-Fidelity 3D Gaussian Generation with Diffusion-Aligned Sparse Representation
Authors:Haorui Ji,Weizhe Liu,Hongdong Li,Hengkai Guo
View a PDF of the paper titled FLUX3D: High-Fidelity 3D Gaussian Generation with Diffusion-Aligned Sparse Representation, by Haorui Ji and 3 other authors
Abstract:Sparse voxel representation has emerged as a scalable foundation for image-to-3D Gaussian Splatting (3DGS) generation, yet current methods struggle to preserve high-frequency visual details of input images due to two structural bottlenecks. First, they adopt discriminative 2D features optimized for semantic abstraction to construct sparse voxel latents, which suppress reconstructive cues and induce a representation bottleneck. Second, in the generation stage, standard diffusion transformers lack effective mechanisms to align dense 2D image tokens with sparse 3D voxel latents, resulting in a cross-modal correspondence bottleneck. To address these issues, we propose FLUX3D, a scalable image-to-3DGS framework that boosts both representation learning and cross-modal alignment during generation. We first revisit 2D feature selection for sparse-voxel-based 3D representation learning, propose Diffusion-Aligned Structured Latents (DA-SLAT) and couple it with a decoder-only architecture to improve 3DGS reconstruction fidelity. We also design a sparse-structure-aware diffusion framework, which integrates the Sparse-structure Multimodal Diffusion Transformer (SMDiT) and Modal-Aware Rotary Positional Embedding (MARoPE) to achieve geometry-agnostic 2D-3D alignment. Extensive benchmark experiments demonstrate that FLUX3D yields substantial improvements in appearance fidelity and significantly outperforms all state-of-the-art (SOTA) methods in generating high-quality 3DGS assets.
| | | | --- | --- | | Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) | | Cite as: |arXiv:2606.24874[cs.CV] | | | (orarXiv:2606.24874v1[cs.CV] for this version) | | |https://doi.org/10.48550/arXiv.2606.24874<br>Focus to learn more<br>arXiv-issued DOI via DataCite |
Submission history
From: Haorui Ji \[view email]
[v1] Tue, 23 Jun 2026 17:52:21 UTC (7,623 KB)
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