HiFi3D: High-Fidelity 3D Asset Editing for Sparse Voxel Representations

Existing 3D editing methods suffer from two critical limitations: optimization-based approaches require lengthy per-instance tuning, while recent flow-based methods struggle to preserve fine geometric structures and material details during editing. We introduce HiFi3D, a training-free 3D editing framework built upon TRELLIS2's factored O-Voxel representation, enabling precise instruction-guided modifications without model retraining. Our method overcomes two fundamental challenges: fragility of sparse voxel topology under stochastic perturbations, and the entanglement between geometric structure and surface appearance in joint editing pipelines. For geometry manipulation, we propose Dual-Space Geometry-Preserving Flow Editing, which combines mask-free regional dynamics with dual-level preservation, maintaining trajectory stability while voxel-supervised alignment ensures decoded geometric consistency through a differentiable decoder. For appearance control, we introduce Geometry-Conditioned Material Editing with PBR consistency constraints, reformulating texture editing as a geometry-aware process where multi-sample velocity estimation stabilizes material flow and decoded-space regularization enforces physically plausible PBR attributes. Extensive experiments demonstrate that HiFi3D achieves accurate instruction-following modifications while outperforming state-of-the-art baselines in both visual quality and geometric fidelity.