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High-resolution 3D geometry refinement built upon Sparc3D.
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Released on October 23, 2026.
Hi3D v1.0 refines a coarse mesh into high-resolution geometry, conditioned on a reference image of the same object. Built upon the Sparc3D representation and refinement architecture, it progressively samples sparse latents, decodes them with a self-pruning sparse VAE, and extracts a mesh.
The project report describes training on approximately two million curated 3D assets and refinement up to an effective sparse extraction-grid resolution of 1536³. The Python package and public API retain the names sparc3d and Sparc3DPipeline for compatibility.
This repository provides inference code. Training code and the paper's evaluation harness are not included. Model artifacts are available on Hugging Face. The examples load hi3d-official/Hi3Dv1.0 directly; a compatible local model directory is also supported.
Image features condition sparse flow on coordinates derived from the coarse geometry. After decoding and mesh extraction, the mesh is revoxelized to provide the sparse support for the next pass.
| Pass | Nominal structural grid | Sparse extraction grid | Transformer |
|---|---|---|---|
| 1 | 64³ | 512³ | transformer_512 |
| 2 | 128³ | 1024³ | transformer_1024 |
| 3 | 192³ | 1536³ | transformer_1024 |
A 512³ request runs one pass; 1024³ runs two; 1536³ runs all three. These labels describe nominal sparse coordinate and extraction grids, rather than a dense 1536³ latent tensor or a fixed triangle count. See pipeline details.
Red boxes mark the reference regions enlarged in each row. Shaded geometry and colored surface-normal renderings show local structures, including layered relief, architectural moldings, curved boundaries, scales, teeth, and concentric grooves. Hi3D v1.0 is highlighted in teal in the rightmost column. Click the figure to inspect the full-resolution image.