ViewRec3D: Learning to Recommend 3D Viewpoints for AI Photography

Viewpoint dictates the photo composition and plays a vital role in AI photography. Existing 3D viewpoint recommendation methods are often limited in viewpoint adjustments, which is mainly due to their lack of high-quality training data with accurate and large 3D view changes. Ideally, the training data should contain suboptimal images and expert-selected optimal viewpoints, but this can be very expensive to curate. To solve this, we build an automatically-generated 3D viewpoint recommendation dataset from expert photos. Specifically, we first consider these expert photos as optimal and outpaint them to provide more information on scene layouts. Next, we explicitly reconstruct the 3D scene. We then render suboptimal images from random viewpoints. Lastly, we introduce a hierarchical data filtering scheme and acquire high-quality data pairs for our ViewRecDB-100K dataset. On top of this, we further introduce a view recommendation model, ViewRecNet, that can predict expert viewpoints from any suboptimal image inputs using a ray-based dense supervision. The proposed method achieves strong results both quantitatively and qualitatively, supported by LLM-based viewpoint preference and human studies. Data and code are available at https://github.com/anonymous1-submission/ViewRec3D.git.