3D Gaussian Splatting (3DGS) has demonstrated impressive performance in synthesizing novel views after training on a given set of viewpoints. However, its rendering quality deteriorates when the synthesized view deviates significantly from the training views. This decline occurs due to (1) the model's difficulty in generalizing to out-of-distribution scenarios and (2) challenges in interpolating fine details caused by substantial resolution changes and occlusions. A notable case of this limitation is close-up view generation--producing views that are significantly closer to the object than those in the training set. To tackle this issue, we propose a novel approach for close-up view generation based by progressively training the 3DGS model with self-generated data. Our solution is based on three key ideas. First, we leverage the See3D model, a recently introduced 3D-aware generative model, to enhance the details of rendered views. Second, we propose a strategy to progressively expand the ``trust regions'' of the 3DGS model and update a set of reference views for See3D. Finally, we introduce a fine-tuning strategy to carefully update the 3DGS model with training data generated from the above schemes. We further define metrics for close-up views evaluation to facilitate better research on this problem. By conducting evaluations on specifically selected scenarios for close-up views, our proposed approach demonstrates a clear advantage over competitive solutions.

本研究解决了3D高斯分布在生成特写视图时的表现下降问题，特别是在视角与训练视角显著偏离时。论文创新性地提出了一种通过自我生成数据渐进训练3DGS模型的方法，并通过结合See3D模型和改进的参考视图策略，显著提升了合成视图的细节质量，验证结果显示该方法优于现有竞争方案。

Close-up-GS：通过渐进自我训练增强3D高斯分布的特写视图合成