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Fysiverse-3D-Vision is proposed, a unified vision-language-geometry framework for generative 3D scene reconstruction and executable asset construction from a single image that establishes a shared representation where spatial reasoning and geometric reconstruction mutually enhance each other, allowing object layouts to be inferred beyond the constraints of individual asset generators.
SpatialGuard transforms 3D spatial text-to-image generation from a mere prompt-following exercise into a verifiable planning and validation process, significantly enhancing spatial accuracy.
By fusing pixel-level pathology images with spatially-resolved gene expression data, STAMP unlocks a new level of molecular specificity in multimodal pathology representation learning.