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Tianjiao Yu

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  1. arXiv

    ELSA3D unifies 3D understanding and generation via elastic semantic anchoring, reaching state-of-the-art image-to-3D, text-to-3D, and 3D captioning while roughly halving FLOPs and inference latency

    ELSA3D introduces elastic semantic anchoring, representing geometry with a scale-aware octree tokenizer and adding sparse cross-modal Anchor Tokens that select semantic cues, route them to the most relevant 3D scale, and write the fused signal back into the unified representation, with a lightweight per-block router making computation and reasoning elastic; it achieves state-of-the-art performance across image-to-3D generation, text-to-3D generation, and 3D captioning, outperforming the strongest unified baseline while roughly halving FLOPs and inference latency relative to the non-elastic version of the same model.

  2. arXiv

    SILSA generates 3D from a single image with 384 sliding-window slice latents, lifting PSNR 8.7% and cutting Betti error 9.2%

    SILSA is a topology-aware high-resolution 3D generation framework that replaces voxel tokens with a fixed set of overlapping slice latents along the three canonical axes, combining a SliceVAE, a Volumetric Anchor Lattice, and slice-level persistent-homology supervision to raise PSNR from 30.12 to 32.74, coverage from 73.12% to 79.08%, and reduce Betti error by 9.2% on image-to-3D generation while using only 384 tokens, cutting training memory by 40.4% and inference time by 58.5%.

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