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Evidence record 641 · automatically gathered

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

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

Record details

Published: 23 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (23 June 2026), “FLUX3D: High-Fidelity 3D Gaussian Generation with Diffusion-Aligned Sparse Representation,” evidence record 641, https://ethics.ai/record/641 (originally published by arXiv).

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