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.
Rgm: Reconstructing high-fidelity 3d car assets with relightable 3d-gs generative model from a single image
3 Pith papers cite this work. Polarity classification is still indexing.
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Asset Harvester converts sparse in-the-wild object observations from AV driving logs into complete simulation-ready 3D assets via data curation, geometry-aware preprocessing, and a SparseViewDiT model that couples sparse-view multiview generation with 3D Gaussian lifting.
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
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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.
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Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation
Asset Harvester converts sparse in-the-wild object observations from AV driving logs into complete simulation-ready 3D assets via data curation, geometry-aware preprocessing, and a SparseViewDiT model that couples sparse-view multiview generation with 3D Gaussian lifting.
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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.