# Camera-Controlled Video Generation

Video diffusion models lose scene geometry when the camera moves or objects move, because cross-frame attention often spreads its response over several regions instead of locking onto the same scene element. GAD extracts correspondence targets from the attention of a frozen geometry foundation model, VGGT-, on clean videos, and routes supervision by noise timestep to the layer where scene structure is forming, training video attention with pairwise and trackwise objectives. On 15-second long-range generation with RealEstate10K and DL3DV-10K, adding GAD to Wan2.2 5B raises cross-frame matching hit rate by at least 30% relative on both datasets, with no extra inference modules or computation.

Video diffusion models lose scene geometry when the camera moves or objects move, because cross-frame attention often spreads its response over several regions instead of locking onto the same scene element. GAD extracts correspondence targets from the attention of a frozen geometry foundation model, VGGT-, on clean videos, and routes supervision by noise timestep to the layer where scene structure is forming, training video attention with pairwise and trackwise objectives. On 15-second long-range generation with RealEstate10K and DL3DV-10K, adding GAD to Wan2.2 5B raises cross-frame matching hit rate by at least 30% relative on both datasets, with no extra inference modules or computation.

In dynamic world modeling, the same observations must condition repeated generation at different times and viewpoints, and prior pipelines rendered the reconstructed scene to RGB and re-encoded it for every request. 4LC binds visual representations of static and dynamic content from several encoders to shared geometry and motion, forming a 4D latent cache that is read directly at independently specified times and viewpoints. Under identical generation settings, conditioning falls from 14.9% to 0.25% of end-to-end generation time, and a single 4LC-Video model covers camera control, temporal control, novel-view synthesis, scene revisits and long-term generation without task-specific fine-tuning.

In dynamic world modeling, the same observations must condition repeated generation at different times and viewpoints, and prior pipelines rendered the reconstructed scene to RGB and re-encoded it for every request. 4LC binds visual representations of static and dynamic content from several encoders to shared geometry and motion, forming a 4D latent cache that is read directly at independently specified times and viewpoints. Under identical generation settings, conditioning falls from 14.9% to 0.25% of end-to-end generation time, and a single 4LC-Video model covers camera control, temporal control, novel-view synthesis, scene revisits and long-term generation without task-specific fine-tuning.

LIFT introduces a unified image-to-video framework that adds Layout-In-FuTure control on top of camera control, letting users specify what should appear in a future view and where; it transfers a dense per-frame layout teacher to a last-frame-layout student via dual-mode on-policy self-distillation (OPSD) and curates the LIFT-Vista dataset for large viewpoint changes, reporting improvements in video quality, future-layout controllability, and camera controllability over compared methods.

LIFT introduces a unified image-to-video framework that adds Layout-In-FuTure control on top of camera control, letting users specify what should appear in a future view and where; it transfers a dense per-frame layout teacher to a last-frame-layout student via dual-mode on-policy self-distillation (OPSD) and curates the LIFT-Vista dataset for large viewpoint changes, reporting improvements in video quality, future-layout controllability, and camera controllability over compared methods.

The work introduces GEAR, which avoids fusing history into a persistent global 3D representation and instead uses per-frame geometry to build token-level history-to-target correspondences, treating geometry as an explicit address for visual memory; Geometric Correspondence Attention then sparsely injects only geometrically matched historical features, while an Invisible Octree rejects projectable but occluded correspondences, reporting state-of-the-art visual quality, camera control, and revisit consistency on DL3DV-Evaluation and WorldScore-Static and enabling minute-long generation.

The work introduces GEAR, which avoids fusing history into a persistent global 3D representation and instead uses per-frame geometry to build token-level history-to-target correspondences, treating geometry as an explicit address for visual memory; Geometric Correspondence Attention then sparsely injects only geometrically matched historical features, while an Invisible Octree rejects projectable but occluded correspondences, reporting state-of-the-art visual quality, camera control, and revisit consistency on DL3DV-Evaluation and WorldScore-Static and enabling minute-long generation.