MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video
  Generation
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.
67a591234020a3bfdb8cb2e5
Jinbo Xing
admin_assigned
2025-02-07T10:43:19.911Z
64770e86d7cf39f2e937ae9a
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Jinbo Xing
user
Doubiiu
67a591234020a3bfdb8cb2e6
Long Mai
67a591234020a3bfdb8cb2e7
Cusuh Ham
admin_assigned
2025-02-07T10:43:42.324Z
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/avatars/783bdae07b2663eebeea4c7919a87c91.svg
Cusuh Ham
user
cusuh
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Jiahui Huang
admin_assigned
2025-02-07T10:44:01.034Z
644a717e75fce8ebef4e4955
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/zLga4NZBohFPlv50dcAo9.png
Jiahui Huang
user
heiwang1997
67a591234020a3bfdb8cb2e9
Aniruddha Mahapatra
admin_assigned
2025-02-07T10:44:08.344Z
633bd831d5935998f74c4156
/avatars/feb4976ad10dd678ccad2652acf8a611.svg
Aniruddha Mahapatra
user
aniruddha26398
67a591234020a3bfdb8cb2ea
Chi-Wing Fu
67a591234020a3bfdb8cb2eb
Tien-Tsin Wong
admin_assigned
2025-02-07T10:44:26.835Z
65574f0fc4865c852d5eec15
/avatars/1e03db4f2de4959dee620c577fbbb063.svg
Tien-Tsin Wong
user
ttwong
67a591234020a3bfdb8cb2ec
Feng Liu
MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video
  Generation
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.
MotionCanvas integrates user-driven controls into image-to-video generation to enable intuitive 3D-aware motion design without requiring 3D training data.
computer graphics video diffusion models image-to-video generation motion-conditioning signals 3D-aware motion control