HiFiVe is a training-free framework using an auto-regressive texture refinement pipeline with depth-based warping, multi-view fusion, and symmetry to enhance both texture and geometry fidelity in vehicle generation from 2D priors.
Drive-1-to-3: Enriching diffusion priors for novel view synthesis of real vehicles,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
3DCarGen synthesizes 3D-consistent multi-view images from one input photo, builds a coarse 3D Gaussian representation, then generates arbitrary views and recovers detailed meshes with color-normal optimization for real-world car images.
citing papers explorer
-
HiFiVe: High-Fidelity Vehicle Generation Leveraging Auto-Regressive 2D Generative Priors
HiFiVe is a training-free framework using an auto-regressive texture refinement pipeline with depth-based warping, multi-view fusion, and symmetry to enhance both texture and geometry fidelity in vehicle generation from 2D priors.
-
3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis
3DCarGen synthesizes 3D-consistent multi-view images from one input photo, builds a coarse 3D Gaussian representation, then generates arbitrary views and recovers detailed meshes with color-normal optimization for real-world car images.