UniRecGen：多视图三维重建与生成的统一框架

摘要

稀疏视角三维建模始终面临重建保真度与生成合理性的根本矛盾。前馈重建方法虽在效率与输入对齐方面表现优异，却常缺乏实现结构完整性所需的全局先验知识；而基于扩散的生成方法虽能提供丰富几何细节，却难以保证多视角一致性。我们提出UniRecGen统一框架，将两种范式整合至协同工作的系统中。为化解坐标空间、三维表征和训练目标的内在冲突，我们在共享规范空间中对齐双模型，采用解耦协同学习策略——既保持训练稳定性，又实现推理阶段的无缝协作。具体而言，重建模块经适配后可提供规范几何锚点，扩散生成器则通过潜在增强条件机制优化并补全几何结构。实验表明，UniRecGen在稀疏观测条件下能生成更完整、一致的三维模型，在保真度与鲁棒性方面均超越现有方法。

English

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. To overcome inherent conflicts in coordinate spaces, 3D representations, and training objectives, we align both models within a shared canonical space. We employ disentangled cooperative learning, which maintains stable training while enabling seamless collaboration during inference. Specifically, the reconstruction module is adapted to provide canonical geometric anchors, while the diffusion generator leverages latent-augmented conditioning to refine and complete the geometric structure. Experimental results demonstrate that UniRecGen achieves superior fidelity and robustness, outperforming existing methods in creating complete and consistent 3D models from sparse observations.

UniRecGen：多视图三维重建与生成的统一框架

UniRecGen: Unifying Multi-View 3D Reconstruction and Generation

摘要

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