AnyI2V - An image and animation generation framework jointly developed by Fudan University and Alibaba DAMO Academy.

What is AnyI2V?

AnyI2V is an innovative image and animation generation framework jointly developed by Fudan University, Alibaba DAMO Academy, and Lakeside Lab. The framework requires no large amount of training data and can transform static conditional images (such as meshes and point clouds) into dynamic videos, supporting user-defined motion trajectories. AnyI2V supports multiple modal inputs and can be flexibly edited via LoRA and text prompts. The framework excels in spatial and motion control, providing an efficient and flexible new method for image animation.

Main functions of AnyI2V

• Multimodal supportIt supports multiple types of conditional inputs, including modalities such as grids and point clouds, which are difficult to obtain paired training data for.
• Mixed conditional inputThe framework can accept different types of conditional input combinations, further increasing the flexibility of input.
• Editing functionEdit the original image using LoRA or different text prompts to achieve style transfer and content adjustment.
• Motion controlIt allows users to control the animation effects of videos by defining motion trajectories, achieving precise motion control.
• No training requiredIt requires no large amount of training data and a complex training process, thus lowering the barrier to entry.

AnyI2V technical principles

• DDIM inversionAnyI2V performs DDIM (Denoising Diffusion Implicit Model) inversion on conditional images. DDIM is a diffusion model that recovers the image by progressively removing noise. During the inversion process, AnyI2V extracts features from the conditional image, which will be used in subsequent animation generation.
• Feature extraction and replacementWhen extracting features, AnyI2V removes the temporal self-attention module from 3D U-Net. This module is mainly used to process temporal information in videos, while conditional images only contain spatial information. Features are extracted from the spatial blocks of 3D U-Net and saved at specific time steps.
• Optimize latent representationAnyI2V replaces the extracted features back into the 3D U-Net to optimize the latent representation. Constraints are applied through automatically generated semantic masks to ensure optimization occurs only in specific regions. The semantic masks are dynamically generated based on the content of the conditional image, improving the accuracy of the generated results.
• Motion controlUsers control animation generation by defining motion trajectories. AnyI2V takes these motion trajectories as input and combines them with optimized latent representations to generate videos that conform to the user-defined motion. This motion control mechanism allows users to precisely control the movement paths of objects in the video.

AnyI2V's project address

• Project official websitehttps://henghuiding.com/AnyI2V/
• GitHub repositoryhttps://github.com/FudanCVL/AnyI2V
• arXiv technical paper: https://arxiv.org/pdf/2507.02857

AnyI2V application scenarios

• Animation ProductionAnimators can quickly generate animation prototypes, directly converting static images into dynamic videos, providing more creative space for animation creation.
• Video effectsIn film and television production, it is used to generate complex visual effects, transforming static scene images into dynamic backgrounds, or adding dynamic effects to characters to enhance visual impact.
• Game developmentGame developers generate dynamic scenes and character animations in the game, bringing richer and more vivid visual effects to the game.
• Dynamic AdsAdvertising designers transform static advertising images into dynamic videos to attract viewers' attention.
• Social media contentBrands and content creators generate compelling social media videos, increasing content reach and user engagement.