Multi-View Video Generation
Overview
Multi-view video generation studies how to create videos that remain coherent across camera viewpoints and over time, enabling novel-view rendering, 3D reconstruction, and interactive simulation. Common approaches extend diffusion or autoregressive video models with camera and pose controls, cross-view attention, shared spatial representations, or geometric cues such as depth, point clouds, and 3D memory. Work spans dynamic human scenes, robot manipulation, and driving world models, with benchmarks including DNA-Rendering, ActorsHQ, Ego-Exo4D, and nuScenes. A central challenge is balancing visual realism and controllability with geometric consistency across large viewpoint changes and long sequences, especially when training data provides only sparse views.
Latest papers
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