AI RESEARCH
UniRecGen: Unifying Multi-View 3D Reconstruction and Generation
arXiv CS.CV
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ArXi:2604.01479v1 Announce Type: new Sparse-view 3D modeling represents a fundamental tension between reconstruction fidelity and generative plausibility. While feed-forward reconstruction excels in efficiency and input alignment, it often lacks the global priors needed for structural completeness. Conversely, diffusion-based generation provides rich geometric details but struggles with multi-view consistency. We present UniRecGen, a unified framework that integrates these two paradigms into a single cooperative system.