HI3D 3.0 (Twinkle3D): Object-specific 3D Asset Generation with High Resolution

Key points

• Image-to-3D generation has become increasingly capable of producing objects that closely resemble the input image, and an outstanding challenge is to reproduce the depicted object itself, including the specific geometry that defines it.1
• Twinkle3D advances high-fidelity geometry generation along four dimensions.1
• Third, subsequent refinement cannot fully compensate for errors introduced during initial generation; we therefore strengthen both global shape and local detail in the initial generation stage, and the resulting single-stage model surpasses prior two-stage pipelines with $512^{3}$ refinement.1
• Finally, we introduce a fine-grained image-3D cross-modal interaction mechanism that strengthens correspondence between visual evidence and geometric tokens, improving the recovery of object-specific structures.1

Sources (1)

• [1]HI3D 3.0 (Twinkle3D): Object-specific 3D Asset Generation with High Resolution
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 10:57 AM

  We present Hi3D 3.0, an image-to-3D generation system targeting object-specific fidelity, with Twinkle3D as its geometry model for generating watertight triangle meshes at $2048^{3}$ resolution.

  Image-to-3D generation has become increasingly capable of producing objects that closely resemble the input image, and an outstanding challenge is to reproduce the depicted object itself, including the specific geometry that defines it.

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