Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models
Conceitos Básicos
Consistent-1-to-3 is a generative framework that significantly mitigates the issue of maintaining 3D consistency across different views in novel view synthesis.
Resumo
Directory:
Introduction
Novel view synthesis from a single image is crucial in 3D object understanding.
Abstract
Consistent-1-to-3 decomposes NVS into two stages for better consistency.
Data Extraction Techniques
Scene Representation Transformer and view-conditioned diffusion model are used for NVS.
Evaluation Metrics and Results
Extensive experiments show the effectiveness of Consistent-1-to-3 over state-of-the-art methods.
Related Work Overview
Review of literature on novel-view synthesis and grouping based on attributes.
Methodology Details
Two-stage model design with scene representation transformer and diffusion model explained.
Experimental Setting
Dataset, metrics, baselines, and implementation details provided.
Ablation Study
Impact of design choices on fidelity and consistency analyzed.
Conclusion
Contribution of Consistent 1-to-3 in efficient novel view synthesis highlighted.
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Consistent-1-to-3
Estatísticas
Zero-shot novel view synthesis (NVS) is an essential problem in 3D object understanding.
The proposed Consistent-1-to-3 framework significantly improves geometric consistency in NVS tasks.