title: MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video
    Generation
summary: This paper presents a method that allows users to design cinematic video
  shots in the context of image-to-video generation. Shot design, a critical
  aspect of filmmaking, involves meticulously planning both camera movements and
  object motions in a scene. However, enabling intuitive shot design in modern
  image-to-video generation systems presents two main challenges: first,
  effectively capturing user intentions on the motion design, where both camera
  movements and scene-space object motions must be specified jointly; and second,
  representing motion information that can be effectively utilized by a video
  diffusion model to synthesize the image animations. To address these
  challenges, we introduce MotionCanvas, a method that integrates user-driven
  controls into image-to-video (I2V) generation models, allowing users to control
  both object and camera motions in a scene-aware manner. By connecting insights
  from classical computer graphics and contemporary video generation techniques,
  we demonstrate the ability to achieve 3D-aware motion control in I2V synthesis
  without requiring costly 3D-related training data. MotionCanvas enables users
  to intuitively depict scene-space motion intentions, and translates them into
  spatiotemporal motion-conditioning signals for video diffusion models. We
  demonstrate the effectiveness of our method on a wide range of real-world image
  content and shot-design scenarios, highlighting its potential to enhance the
  creative workflows in digital content creation and adapt to various image and
  video editing applications.
paper_authors._id: 67a591234020a3bfdb8cb2e5
paper_authors.name: Jinbo Xing
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-07T10:43:19.911Z
paper_authors._id: 64770e86d7cf39f2e937ae9a
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paper_authors.fullname: Jinbo Xing
paper_authors.type: user
paper_authors.user: Doubiiu
paper_authors._id: 67a591234020a3bfdb8cb2e6
paper_authors.name: Long Mai
paper_authors._id: 67a591234020a3bfdb8cb2e7
paper_authors.name: Cusuh Ham
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-07T10:43:42.324Z
paper_authors._id: 6372fab1bd1595ae66a62543
paper_authors.avatarUrl: /avatars/783bdae07b2663eebeea4c7919a87c91.svg
paper_authors.fullname: Cusuh Ham
paper_authors.type: user
paper_authors.user: cusuh
paper_authors._id: 67a591234020a3bfdb8cb2e8
paper_authors.name: Jiahui Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-07T10:44:01.034Z
paper_authors._id: 644a717e75fce8ebef4e4955
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/zLga4NZBohFPlv50dcAo9.png
paper_authors.fullname: Jiahui Huang
paper_authors.type: user
paper_authors.user: heiwang1997
paper_authors._id: 67a591234020a3bfdb8cb2e9
paper_authors.name: Aniruddha Mahapatra
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-07T10:44:08.344Z
paper_authors._id: 633bd831d5935998f74c4156
paper_authors.avatarUrl: /avatars/feb4976ad10dd678ccad2652acf8a611.svg
paper_authors.fullname: Aniruddha Mahapatra
paper_authors.type: user
paper_authors.user: aniruddha26398
paper_authors._id: 67a591234020a3bfdb8cb2ea
paper_authors.name: Chi-Wing Fu
paper_authors._id: 67a591234020a3bfdb8cb2eb
paper_authors.name: Tien-Tsin Wong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-07T10:44:26.835Z
paper_authors._id: 65574f0fc4865c852d5eec15
paper_authors.avatarUrl: /avatars/1e03db4f2de4959dee620c577fbbb063.svg
paper_authors.fullname: Tien-Tsin Wong
paper_authors.type: user
paper_authors.user: ttwong
paper_authors._id: 67a591234020a3bfdb8cb2ec
paper_authors.name: Feng Liu
paper_title: MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video
    Generation
paper_summary: This paper presents a method that allows users to design cinematic video
  shots in the context of image-to-video generation. Shot design, a critical
  aspect of filmmaking, involves meticulously planning both camera movements and
  object motions in a scene. However, enabling intuitive shot design in modern
  image-to-video generation systems presents two main challenges: first,
  effectively capturing user intentions on the motion design, where both camera
  movements and scene-space object motions must be specified jointly; and second,
  representing motion information that can be effectively utilized by a video
  diffusion model to synthesize the image animations. To address these
  challenges, we introduce MotionCanvas, a method that integrates user-driven
  controls into image-to-video (I2V) generation models, allowing users to control
  both object and camera motions in a scene-aware manner. By connecting insights
  from classical computer graphics and contemporary video generation techniques,
  we demonstrate the ability to achieve 3D-aware motion control in I2V synthesis
  without requiring costly 3D-related training data. MotionCanvas enables users
  to intuitively depict scene-space motion intentions, and translates them into
  spatiotemporal motion-conditioning signals for video diffusion models. We
  demonstrate the effectiveness of our method on a wide range of real-world image
  content and shot-design scenarios, highlighting its potential to enhance the
  creative workflows in digital content creation and adapt to various image and
  video editing applications.
paper_ai_summary: MotionCanvas integrates user-driven controls into image-to-video generation to enable intuitive 3D-aware motion design without requiring 3D training data.
paper_ai_keywords: computer graphics video diffusion models image-to-video generation motion-conditioning signals 3D-aware motion control