TriMotion: Modality-Agnostic Camera Control for Video Generation

Camera motion control is essential for directing viewpointchanges in generative systems. However, existing methods typically condi-tion the generation process on a single specific modality, such as explicitpose trajectories or reference videos, limiting their ability to support het-erogeneous user inputs. To address this limitation, we present TriMotion,a modality-agnostic framework for camera-controlled video generationthat maps video, pose, and text inputs, describing the same cameratrajectory into a shared motion embedding space. Learning such a spacerequires synchronized supervision across modalities. Therefore, we buildthe Motion Triplet Dataset by extending a Multi-Cam Video Dataset withgeometry-grounded motion descriptions derived from camera extrinsics.We further introduce a latent motion consistency objective that lever-ages the motion embedding space to encourage the generated video tofollow the target camera trajectory directly in latent space, avoiding thecost of pixel-space decoding. Extensive experiments show that TriMotiongenerates high-quality videos that accurately follow the target cameratrajectories across all three modalities. Beyond standard generation, theshared motion embedding space also enables flexible applications such assequential motion composition and cross-modal motion interpolation.