Navigational Compositional Guidance: Learning Content-Adaptive Video Trajectories for Photography

Traditional image composition methods rely on basic geometric transformations and classical aesthetic rules for optimization. With the development of multimodal models, numerous effective composition recommendation methods have been proposed recently. However, most existing methods merely provide static image-based composition references with simple text instructions, which cannot deliver refined and practical guidance for real shooting scenarios. Video-based composition guidance is more consistent with real-world dynamic rules due to video's inherent spatial-temporal continuity. Nevertheless, current video composition methods suffer from limited generalization for complex composition tasks, only supporting basic camera and perspective adjustments, and cannot meet fine-grained guidance demands. To tackle these drawbacks, this paper proposes a novel video model-based content-adaptive composition path recommendation algorithm, named Navigational Composition Guidance. It achieves complex-scene dynamic composition guidance and realizes autonomous navigation within the high-dimensional aesthetic manifold to search for optimal composition paths.