Hi3D open-sources Hi3D v1.0, high-resolution 3D geometry refinement built on Sparc3D, with a technical report
Hi3D v1.0 is described as a model that refines a coarse mesh into high-resolution geometry, conditioned on a reference image of the same object. It builds on the Sparc3D representation and refinement architecture, progressively sampling sparse latents, decoding them with a self-pruning sparse VAE, and extracting a mesh. The project report describes training on about two million curated 3D assets and refinement up to an effective sparse extraction-grid resolution of 1536³. The repository provides inference code, with training code and the evaluation harness not included; model artifacts are hosted on Hugging Face.
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Hi3D v1.0 is described as a model that refines a coarse mesh into high-resolution geometry, conditioned on a reference image of the same object. It builds on the Sparc3D representation and refinement architecture, progressively sampling sparse latents, decoding them with a self-pruning sparse VAE, and extracting a mesh. The project report describes training on about two million curated 3D assets and refinement up to an effective sparse extraction-grid resolution of 1536³. The repository provides inference code, with training code and the evaluation harness not included; model artifacts are hosted on Hugging Face.
Key findings from the stored brief
- Refines a coarse mesh into high-resolution geometry conditioned on a reference image of the same object.
- Report describes training on approximately two million curated 3D assets.
- Refinement reaches an effective sparse extraction-grid resolution of 1536³.
- Repository provides inference code and model weights; training code and evaluation harness are not included.
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Why this may matter
Open inference code and weights let researchers and practitioners test high-resolution 3D geometry refinement directly, while the technical report documents the scale and resolution claims.
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