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MotionCtrl: A Unified and Flexible Motion Controller for Video Generation

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arxiv 2312.03641 v2 pith:DJAMUC5H submitted 2023-12-06 cs.CV cs.AIcs.LGcs.MM

classification cs.CVcs.AIcs.LGcs.MM
keywords motioncameramotionctrlobjectcontrolvideoflexiblegeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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Motions in a video primarily consist of camera motion, induced by camera movement, and object motion, resulting from object movement. Accurate control of both camera and object motion is essential for video generation. However, existing works either mainly focus on one type of motion or do not clearly distinguish between the two, limiting their control capabilities and diversity. Therefore, this paper presents MotionCtrl, a unified and flexible motion controller for video generation designed to effectively and independently control camera and object motion. The architecture and training strategy of MotionCtrl are carefully devised, taking into account the inherent properties of camera motion, object motion, and imperfect training data. Compared to previous methods, MotionCtrl offers three main advantages: 1) It effectively and independently controls camera motion and object motion, enabling more fine-grained motion control and facilitating flexible and diverse combinations of both types of motion. 2) Its motion conditions are determined by camera poses and trajectories, which are appearance-free and minimally impact the appearance or shape of objects in generated videos. 3) It is a relatively generalizable model that can adapt to a wide array of camera poses and trajectories once trained. Extensive qualitative and quantitative experiments have been conducted to demonstrate the superiority of MotionCtrl over existing methods. Project Page: https://wzhouxiff.github.io/projects/MotionCtrl/

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rays as Pixels: Learning A Joint Distribution of Videos and Camera Trajectories

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    Encoding cameras as pixel-aligned raxels lets one video diffusion model jointly denoise video and trajectories, supporting pose estimation, controlled generation, and joint synthesis.

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    Users control a text-to-video model by arranging 3D bounding boxes and camera motion, and the system renders depth maps that steer the diffusion model to generate matching object and camera motion.

  3. GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control

    cs.CV 2025-05 conditional novelty 6.0 of 10

    GeoDrive conditions a frozen video diffusion model on a 3D-rendered version of the requested ego trajectory, cutting trajectory-following error by 42% versus Vista while using 99.7% less training data.

  4. DanceTogether! Identity-Preserving Multi-Person Interactive Video Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A diffusion model fuses per-person masks with pose keypoints to generate identity-preserving, two-person interactive videos from a single reference image, outperforming prior single-person-animation pipelines.

  5. VIGOR: VIdeo Geometry-Oriented Reward for Temporal Generative Alignment

    cs.CV 2026-03 conditional novelty 5.5 of 10

    A VGGT-based pointwise reprojection reward with geometry-aware sampling improves video geometric consistency via SFT/DPO and causal test-time search.

  6. Tora2: Motion and Appearance Customized Diffusion Transformer for Multi-Entity Video Generation

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Tora2 adds decoupled personalization embeddings, gated self-attention binding, and contrastive learning to Tora, enabling simultaneous appearance and trajectory customization for multiple entities in generated video.

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