Cascaded Dynamic Memory Refinement and Semantic Alignment for Exo-to-Ego Cross-View Video Generation

Cross-view video generation from exocentric (third-person) to egocentric (first-person) perspectives poses a challenging task, due to the significant viewpoint gap and limited overlap between these two views. Previous methods exhibit limitations in capturing long-range temporal context and overlook egocentric semantic priors, leading to degraded performance in cross-view synthesis. To address these challenges, we propose a cue-free video-based approach termed cascaded Dynamic memory Refinement and Semantic Alignment (DRSA), which integrates temporal knowledge over extended periods and learns egocentric semantic information to generate videos. The Dynamic Memory Refinement (DMR) exploits long horizon temporal dynamics to learn salient information that compensates for the limited overlap between views. Specifically, we devise a dynamic memory that serves as a knowledge repository, and utilize a sliding window to locate the corresponding long-term temporal information, which is subsequently processed with adaptive weighting and cross-attention transformer to refine feature representations. Furthermore, aware of the considerable viewpoint divergence that hinder semantic learning of target view, we propose Viewpoint-aware Semantic Alignment (VSA) with dual encoder-decoder learning and semantic alignment, which transfer egocentric semantic details from the egocentric synthesis pipeline to the exocentric synthesis pipeline. In particular, the VSA module narrows the semantic gap between views, further promoting long-range temporal modeling in DMR under alignment constraints. By extending this into a cascaded fashion, the Cascaded Alignment and Refinement (CAR) progressively aligns semantic features and performs feature refinement to facilitate viewpoint learning at different levels of granularity. To overcome the limitations of existing databases known for their limited static scenes and scarcity of interacting objects, we create a new dataset with dynamic exocentric scenes and rich interacting objects to further promote the task. Thorough experimental analysis reveals that our method surpasses current state-of-the-art techniques in terms of both quantitative metrics and qualitative evaluations.