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FLUX3D introduces a framework for high-fidelity image-to-3D Gaussian Splatting generation by enhancing representation learning and cross-modal alignment with diffusion-aligned structured latents and a sparse-structure-aware diffusion transformer, achieving state-of-the-art results.
This paper investigates the Platonic Representation Hypothesis, proposing that alignment arises from linear structure in representations, and introduces a statistical framework of signal, bias, and noise.