AnyI2V: Animating Any Conditional Image with Motion Control
Recent advancements in video generation, particularly in diffusion models,
have driven notable progress in text-to-video (T2V) and image-to-video (I2V)
synthesis. However, challenges remain in effectively integrating dynamic motion
signals and flexible spatial constraints. Existing T2V methods typically rely
on text prompts, which inherently lack precise control over the spatial layout
of generated content. In contrast, I2V methods are limited by their dependence
on real images, which restricts the editability of the synthesized content.
Although some methods incorporate ControlNet to introduce image-based
conditioning, they often lack explicit motion control and require
computationally expensive training. To address these limitations, we propose
AnyI2V, a training-free framework that animates any conditional images with
user-defined motion trajectories. AnyI2V supports a broader range of modalities
as the conditional image, including data types such as meshes and point clouds
that are not supported by ControlNet, enabling more flexible and versatile
video generation. Additionally, it supports mixed conditional inputs and
enables style transfer and editing via LoRA and text prompts. Extensive
experiments demonstrate that the proposed AnyI2V achieves superior performance
and provides a new perspective in spatial- and motion-controlled video
generation. Code is available at https://henghuiding.com/AnyI2V/.
68788cd9001546c83aa4f9f7
Ziye Li
admin_assigned
2025-07-17T09:48:49.437Z
66aef8691dd7d0a8c6584724
/avatars/df9c2a56f3d0746cf64a330137a105b4.svg
Ziye Li
user
TribeRinb
68788cd9001546c83aa4f9f8
Hao Luo
68788cd9001546c83aa4f9f9
Xincheng Shuai
admin_assigned
2025-07-17T09:48:34.400Z
6335c7fa2db86a181cca723f
/avatars/13f04af01914f8473f9939f49b4eecd4.svg
XC
user
XinchengShuai
68788cd9001546c83aa4f9fa
Henghui Ding
admin_assigned
2025-07-17T09:48:26.454Z
67ff29ecbf6889a333c69c7a
https://cdn-avatars.huggingface.co/v1/production/uploads/67ff29ecbf6889a333c69c7a/zilMQrxIgUKYvHBVCHaKL.jpeg
Henghui Ding
user
HenghuiDing
AnyI2V: Animating Any Conditional Image with Motion Control
Recent advancements in video generation, particularly in diffusion models,
have driven notable progress in text-to-video (T2V) and image-to-video (I2V)
synthesis. However, challenges remain in effectively integrating dynamic motion
signals and flexible spatial constraints. Existing T2V methods typically rely
on text prompts, which inherently lack precise control over the spatial layout
of generated content. In contrast, I2V methods are limited by their dependence
on real images, which restricts the editability of the synthesized content.
Although some methods incorporate ControlNet to introduce image-based
conditioning, they often lack explicit motion control and require
computationally expensive training. To address these limitations, we propose
AnyI2V, a training-free framework that animates any conditional images with
user-defined motion trajectories. AnyI2V supports a broader range of modalities
as the conditional image, including data types such as meshes and point clouds
that are not supported by ControlNet, enabling more flexible and versatile
video generation. Additionally, it supports mixed conditional inputs and
enables style transfer and editing via LoRA and text prompts. Extensive
experiments demonstrate that the proposed AnyI2V achieves superior performance
and provides a new perspective in spatial- and motion-controlled video
generation. Code is available at https://henghuiding.com/AnyI2V/.
AnyI2V is a training-free framework that animates conditional images with user-defined motion trajectories, supporting various data types and enabling flexible video generation.
diffusion models text-to-video image-to-video ControlNet motion trajectories conditional images meshes point clouds mixed conditional inputs style transfer LoRA text prompts