HiFiVe: High-Fidelity Vehicle Generation Leveraging Auto-Regressive 2D Generative Priors

2026-06-23

AI Summary

Existing 3D vehicle generation methods suffer from low geometric fidelity and blurry textures, limiting their utility for downstream tasks. This work proposes a training-free optimization framework that leverages 2D generative priors anchored by 3D geometric constraints to jointly refine geometry and texture. A novel autoregressive texture refinement pipeline is introduced, integrating depth-guided multi-view fusion and vehicle symmetry priors to enforce cross-view consistency and mitigate error accumulation. Furthermore, high-frequency geometric details are recovered via normal map inversion, enabling mesh refinement. Evaluated on both synthetic and real-world vehicle datasets, the proposed method significantly outperforms current state-of-the-art approaches, achieving notable improvements in both geometric accuracy and texture quality.

Abstract

Existing 3D vehicle generation methods often suffer from low geometric fidelity and blurry textures, hindering their downstream applications. While recent works adopt multi-view diffusion models for high-fidelity texture, they are often constrained by fixed viewpoints, limited resolution, and a reliance on costly fine-tuning to achieve cross-view consistency. In this paper, we propose HiFiVe, a training-free framework for high-fidelity vehicle modeling through joint texture and geometry enhancement by imposing 3D geometric constraints to anchor 2D generative priors. Specifically, we propose an auto-regressive texture refinement pipeline that progressively synthesizes high-resolution textures from arbitrary viewpoints. To ensure cross-view consistency, the coarse geometry serves as a synchronization prior, conditioning each generation step on previously synthesized frames via depth-based warping and multi-view texture fusion. Moreover, the inherent symmetry of vehicles is exploited to mitigate error accumulation. Finally, high-frequency surface details are recovered by refining the mesh geometry using normal maps estimated from the enhanced textures. Extensive experiments on synthetic and real-world vehicle datasets demonstrate that our method significantly improves both geometric detail and texture quality compared to state-of-the-art baselines.

Problem

• 3D vehicle generation
• geometric fidelity
• texture quality
• cross-view consistency
• high-fidelity synthesis

Innovation

• auto-regressive generation
• 3D geometric constraints
• multi-view consistency
• texture-geometry fusion
• training-free framework