AnyI2V: Animating Any Conditional Image with Motion Control

2025-07-03

AI Summary

Existing video generation methods—text-to-video (T2V) and image-to-video (I2V)—struggle to simultaneously achieve precise spatial layout control and faithful dynamic motion modeling: T2V lacks explicit spatial constraints; I2V suffers from poor editability; and ControlNet-style approaches are limited by image-only conditioning, absence of explicit motion control, high training overhead, and incompatibility with non-image modalities (e.g., meshes, point clouds). This paper proposes a training-free diffusion framework that enables user-specified motion trajectories to drive animation generation from arbitrary modality inputs—including images, 3D meshes, and point clouds—while integrating textual prompts for style transfer and semantic editing. Its core innovation lies in the first realization of explicit motion control and seamless multimodal conditional fusion under non-image modalities. Experiments demonstrate substantial improvements over state-of-the-art methods in spatial fidelity, motion controllability, and editing flexibility, enabling zero-cost, customized high-quality video synthesis.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Generation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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/.

Problem

• Integrating dynamic motion signals in video generation
• Overcoming limitations of text and image-based video synthesis
• Enabling flexible spatial and motion control without training

Innovation

• Training-free framework for motion-controlled video generation
• Supports diverse conditional inputs like meshes and point clouds
• Enables style transfer and editing via LoRA and text prompts