ArXiv

FLUX3D: High-Fidelity 3D Gaussian Generation with Diffusion-Aligned Sparse Representation

2026-06-23 · 17:52 UTC ·Haorui Ji, Weizhe Liu, Hongdong Li... ·1 min read
Authors
Haorui Ji, Weizhe Liu, Hongdong Li...
Categories
cs.CV, cs.AI
arXiv
https://arxiv.org/abs/2606.24874v1
PDF
https://arxiv.org/pdf/2606.24874v1

Brief

FLUX3D (Ji et al., 2026) tackles image-to-3D Gaussian Splatting by addressing two bottlenecks: discriminative 2D features that harm reconstruction and weak diffusion-based 2D→3D alignment. The paper proposes DA-SLAT with a decoder-only backbone and a sparse-structure-aware diffusion stack (SMDiT + MARoPE). Benchmark results (per abstract) show substantial appearance-fidelity gains and SOTA performance. Summary based on the abstract.

Why it matters

FLUX3D (Haorui Ji et al., arXiv:2606.24874v1, published 2026-06-23) introduces Diffusion-Aligned Structured Latents (DA-SLAT) paired with a decoder-only architecture to boost sparse-voxel 3D Gaussian Splatting (3DGS) reconstruction fidelity.

Key details

  • The method adds a sparse-structure-aware diffusion framework combining a Sparse-structure Multimodal Diffusion Transformer (SMDiT) and Modal-Aware Rotary Positional Embedding (MARoPE) to achieve geometry-agnostic 2D→3D alignment.
  • Authors report extensive benchmark experiments showing substantial appearance-fidelity improvements and state-of-the-art results, claiming FLUX3D significantly outperforms prior SOTA 3DGS generation methods.
Source evidence

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.