GimbalDiffusion: Gravity-Aware Camera Control for Video Generation
Recent progress in text-to-video generation has achieved remarkable realism, yet fine-grained control over camera motion and orientation remains elusive. Existing approaches typically encode camera trajectories through relative or ambiguous representations, limiting explicit geometric control. We introduce GimbalDiffusion, a framework that enables camera control grounded in physical-world coordinates, using gravity as a global reference. Instead of describing motion relative to previous frames, our method defines camera trajectories in an absolute coordinate system, allowing precise and interpretable control over camera parameters without requiring an initial reference frame. We leverage panoramic 360-degree videos to construct a wide variety of camera trajectories, well beyond the predominantly straight, forward-facing trajectories seen in conventional video data. To further enhance camera guidance, we introduce null-pitch conditioning, an annotation strategy that reduces the model's reliance on text content when conflicting with camera specifications (e.g., generating grass while the camera points towards the sky). Finally, we establish a benchmark for camera-aware video generation by rebalancing SpatialVID-HQ for comprehensive evaluation under wide camera pitch variation. Together, these contributions advance the controllability and robustness of text-to-video models, enabling precise, gravity-aligned camera manipulation within generative frameworks.
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Frédéric Fortier-Chouinard
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2025-12-12T09:16:49.964Z
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Frédéric Fortier-Chouinard
lefreud
user
lefreud
693b0202804e33f037cf8e66
Yannick Hold-Geoffroy
693b0202804e33f037cf8e67
Valentin Deschaintre
693b0202804e33f037cf8e68
Matheus Gadelha
693b0202804e33f037cf8e69
Jean-François Lalonde
GimbalDiffusion: Gravity-Aware Camera Control for Video Generation
Recent progress in text-to-video generation has achieved remarkable realism, yet fine-grained control over camera motion and orientation remains elusive. Existing approaches typically encode camera trajectories through relative or ambiguous representations, limiting explicit geometric control. We introduce GimbalDiffusion, a framework that enables camera control grounded in physical-world coordinates, using gravity as a global reference. Instead of describing motion relative to previous frames, our method defines camera trajectories in an absolute coordinate system, allowing precise and interpretable control over camera parameters without requiring an initial reference frame. We leverage panoramic 360-degree videos to construct a wide variety of camera trajectories, well beyond the predominantly straight, forward-facing trajectories seen in conventional video data. To further enhance camera guidance, we introduce null-pitch conditioning, an annotation strategy that reduces the model's reliance on text content when conflicting with camera specifications (e.g., generating grass while the camera points towards the sky). Finally, we establish a benchmark for camera-aware video generation by rebalancing SpatialVID-HQ for comprehensive evaluation under wide camera pitch variation. Together, these contributions advance the controllability and robustness of text-to-video models, enabling precise, gravity-aligned camera manipulation within generative frameworks.
GimbalDiffusion framework enables precise camera control in text-to-video generation using absolute coordinates and gravity as a reference, enhancing controllability and robustness.
GimbalDiffusion camera trajectories physical-world coordinates gravity absolute coordinate system panoramic 360-degree videos null-pitch conditioning SpatialVID-HQ camera pitch variation