TriMotion: Modality-Agnostic Camera Control for Video Generation

TriMotion introduces a modality-agnostic framework enabling video generation controlled through multiple input types—video, pose trajectories, or text—by mapping them to a shared motion embedding space. The approach includes a new Motion Triplet Dataset and latent motion consistency objectives, achieving high-fidelity camera-controlled video generation with applications in motion composition and cross-modal interpolation.

TriMotion addresses a fundamental limitation in current video generation systems: their dependence on single-modality inputs for camera control. While existing generative models excel at creating videos, they typically require users to input camera instructions in one specific format, creating friction for diverse creative workflows. This research tackles heterogeneous input handling by developing a unified motion embedding space that translates video, pose, and text descriptions into a common representation, enabling seamless interoperability across modalities.

The technical foundation relies on synchronized cross-modal supervision, which the authors enable through the Motion Triplet Dataset—an augmented collection of multi-camera videos paired with geometry-grounded motion descriptions derived from camera extrinsics. This dataset construction represents significant groundwork that will likely benefit future research in camera control. The latent motion consistency objective represents an efficiency innovation, operating in latent space rather than pixel space to reduce computational overhead during generation.

The implications extend beyond standard video generation. The shared embedding space unlocks novel creative capabilities like sequential motion composition and cross-modal interpolation, where users could blend camera movements described in different formats or chain multiple motion instructions. These features address real production workflows where different team members or tools contribute camera specifications in varying formats.

For AI video generation markets, this work signals maturation toward more intuitive, flexible creative tools. The modality-agnostic approach aligns with broader industry trends toward universal interfaces and cross-modal learning. Developer adoption depends on implementation accessibility and performance metrics compared to single-modality baselines, which the paper demonstrates but requires third-party validation.

• →TriMotion enables camera control in video generation through multiple input modalities (video, pose, text) mapped to a unified motion embedding space.
• →The Motion Triplet Dataset provides synchronized cross-modal supervision crucial for training modality-agnostic camera control systems.
• →Latent space motion consistency objectives reduce computational costs by avoiding pixel-space decoding during video generation.
• →Shared motion embeddings unlock applications beyond standard generation, including sequential motion composition and cross-modal interpolation.
• →The framework demonstrates improved flexibility for creative workflows requiring diverse camera specification methods.