ViewRobust-3D: Benchmarking and Improving Viewpoint Robustness in Single-View 3D Generation

In recent years, single-view 3D generation has advanced rapidly and shown great potential in many areas. However, existing models are primarily developed and evaluated under canonical views, leaving their robustness to broader view variations poorly understood. We observe that non-canonical inputs (e.g., high elevation or non-zero roll angles) can lead to geometric distortions and structural errors. Motivated by this observation, we introduce ViewRobust-3D, a controlled benchmark to systematically analyze the impact of view variations on single-view 3D generation. By isolating view factors through controlled changes in azimuth, elevation, and roll, the benchmark reveals performance degradation and interactions between these factors across multiple models. Guided by these findings, we further propose ViewNorm, a lightweight view normalization strategy to normalize non-canonical inputs toward canonical views. Experiments validate ViewRobust-3D in exposing robustness gaps and demonstrate the effectiveness of ViewNorm in improving the performance of single-view 3D generation models under non-canonical views. Our benchmark and method will be publicly released upon publication.