title: MotionPro: A Precise Motion Controller for Image-to-Video Generation
summary: Animating images with interactive motion control has garnered popularity for
  image-to-video (I2V) generation. Modern approaches typically rely on large
  Gaussian kernels to extend motion trajectories as condition without explicitly
  defining movement region, leading to coarse motion control and failing to
  disentangle object and camera moving. To alleviate these, we present MotionPro,
  a precise motion controller that novelly leverages region-wise trajectory and
  motion mask to regulate fine-grained motion synthesis and identify target
  motion category (i.e., object or camera moving), respectively. Technically,
  MotionPro first estimates the flow maps on each training video via a tracking
  model, and then samples the region-wise trajectories to simulate inference
  scenario. Instead of extending flow through large Gaussian kernels, our
  region-wise trajectory approach enables more precise control by directly
  utilizing trajectories within local regions, thereby effectively characterizing
  fine-grained movements. A motion mask is simultaneously derived from the
  predicted flow maps to capture the holistic motion dynamics of the movement
  regions. To pursue natural motion control, MotionPro further strengthens video
  denoising by incorporating both region-wise trajectories and motion mask
  through feature modulation. More remarkably, we meticulously construct a
  benchmark, i.e., MC-Bench, with 1.1K user-annotated image-trajectory pairs, for
  the evaluation of both fine-grained and object-level I2V motion control.
  Extensive experiments conducted on WebVid-10M and MC-Bench demonstrate the
  effectiveness of MotionPro. Please refer to our project page for more results:
  https://zhw-zhang.github.io/MotionPro-page/.
paper_authors._id: 6835d1587ba57211f250371d
paper_authors.name: Zhongwei Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T15:48:53.306Z
paper_authors._id: 6496f5754a3c31df8e3139f6
paper_authors.avatarUrl: /avatars/cf789d1986f976373c82b2976df4542a.svg
paper_authors.fullname: Zhongwei Zhang
paper_authors.type: user
paper_authors.user: zzwustc
paper_authors._id: 6835d1587ba57211f250371e
paper_authors.name: Fuchen Long
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:43:53.687Z
paper_authors._id: 6449f2dfeb7db8f70fb990f8
paper_authors.avatarUrl: /avatars/72c02e754bb35dab05c4a5f1e69c95f1.svg
paper_authors.fullname: Fuchen
paper_authors.type: user
paper_authors.user: FireCRT
paper_authors._id: 6835d1587ba57211f250371f
paper_authors.name: Zhaofan Qiu
paper_authors._id: 6835d1587ba57211f2503720
paper_authors.name: Yingwei Pan
paper_authors._id: 6835d1587ba57211f2503721
paper_authors.name: Wu Liu
paper_authors._id: 6835d1587ba57211f2503722
paper_authors.name: Ting Yao
paper_authors._id: 6835d1587ba57211f2503723
paper_authors.name: Tao Mei
paper_title: MotionPro: A Precise Motion Controller for Image-to-Video Generation
paper_summary: Animating images with interactive motion control has garnered popularity for
  image-to-video (I2V) generation. Modern approaches typically rely on large
  Gaussian kernels to extend motion trajectories as condition without explicitly
  defining movement region, leading to coarse motion control and failing to
  disentangle object and camera moving. To alleviate these, we present MotionPro,
  a precise motion controller that novelly leverages region-wise trajectory and
  motion mask to regulate fine-grained motion synthesis and identify target
  motion category (i.e., object or camera moving), respectively. Technically,
  MotionPro first estimates the flow maps on each training video via a tracking
  model, and then samples the region-wise trajectories to simulate inference
  scenario. Instead of extending flow through large Gaussian kernels, our
  region-wise trajectory approach enables more precise control by directly
  utilizing trajectories within local regions, thereby effectively characterizing
  fine-grained movements. A motion mask is simultaneously derived from the
  predicted flow maps to capture the holistic motion dynamics of the movement
  regions. To pursue natural motion control, MotionPro further strengthens video
  denoising by incorporating both region-wise trajectories and motion mask
  through feature modulation. More remarkably, we meticulously construct a
  benchmark, i.e., MC-Bench, with 1.1K user-annotated image-trajectory pairs, for
  the evaluation of both fine-grained and object-level I2V motion control.
  Extensive experiments conducted on WebVid-10M and MC-Bench demonstrate the
  effectiveness of MotionPro. Please refer to our project page for more results:
  https://zhw-zhang.github.io/MotionPro-page/.
paper_ai_summary: MotionPro uses region-wise trajectories and motion masks for precise image-to-video generation, enhancing motion control and disentangling object and camera movement.
paper_ai_keywords: Gaussian kernels motion control motion trajectories motion mask flow maps tracking model feature modulation fine-grained motion MC-Bench WebVid-10M video denoising