Let Your Image Move with Your Motion! -- Implicit Multi-Object Multi-Motion Transfer
FlexiMMT achieves multi-object multi-motion transfer using Motion Decoupled Mask Attention, improving motion fidelity by 23%.
Key Findings
Methodology
FlexiMMT employs Motion Decoupled Mask Attention and Differentiated Mask Propagation Mechanism to achieve multi-object multi-motion transfer. It extracts implicit motion representations from reference videos and assigns them using object-specific masks, ensuring motion and text tokens only affect designated regions.
Key Results
- FlexiMMT improved motion fidelity by 23% and achieved the highest motion transfer accuracy on 200 video-image pairs.
- Compared to existing methods, FlexiMMT also excelled in appearance and temporal consistency.
- Ablation studies confirmed the critical role of Motion Decoupled Mask Attention in motion assignment.
Significance
This research is significant in academia and industry, addressing the challenge of multi-object multi-motion transfer for the first time. FlexiMMT's flexibility and precision make it widely applicable in film production, animation, and other fields.
Technical Contribution
FlexiMMT introduces Motion Decoupled Mask Attention and Differentiated Mask Propagation Mechanism, significantly improving the accuracy and flexibility of multi-object motion transfer. Unlike existing methods, it avoids explicit motion preprocessing, reducing computational complexity.
Novelty
FlexiMMT is the first implicit motion framework to achieve multi-object multi-motion transfer. Its innovation lies in precise motion assignment through mask mechanisms, avoiding cross-object motion entanglement compared to existing single-object methods.
Limitations
- FlexiMMT may underperform in complex backgrounds or fast motion scenarios, requiring further optimization.
- The method heavily relies on the quality of initial masks, which may affect final results.
Future Work
Future research could explore more efficient mask generation methods and applications in real-time video processing.
AI Executive Summary
Existing video generation methods struggle with multi-object multi-motion scenarios. FlexiMMT addresses this issue by introducing Motion Decoupled Mask Attention and Differentiated Mask Propagation Mechanism. This method independently extracts motion representations from multiple reference videos and accurately assigns them to different objects, supporting flexible motion recombination and arbitrary motion-object mappings.
FlexiMMT's core technologies include object-specific mask generation and motion assignment, ensuring motion and text tokens only affect designated regions. Extensive experiments demonstrate FlexiMMT's superior performance in motion fidelity, appearance consistency, and temporal consistency, with a 23% improvement in motion fidelity.
While FlexiMMT has made significant progress in multi-object multi-motion transfer, there is room for improvement in handling complex backgrounds or fast motion. Future research could explore more efficient mask generation methods and applications in real-time video processing.
Deep Analysis
Background
Video generation technology has made significant progress in recent years, especially in controllable video generation. However, existing methods mainly focus on single-object scenarios and struggle when multiple objects require distinct motion patterns. FlexiMMT aims to solve this problem by achieving multi-object multi-motion transfer through an implicit motion transfer framework.
Core Problem
The core problem of multi-object multi-motion transfer is how to independently extract and assign motion representations, avoiding cross-object motion entanglement. Solving this problem is crucial for improving the flexibility and accuracy of video generation.
Innovation
FlexiMMT's core innovations include the introduction of Motion Decoupled Mask Attention and Differentiated Mask Propagation Mechanism. The former achieves precise motion assignment through object-specific masks, while the latter generates object-specific masks directly from diffusion attention and efficiently propagates them across frames.
Methodology
- �� Motion Decoupled Mask Attention: Uses object-specific masks to constrain attention, ensuring motion and text tokens only affect designated regions.
- �� Differentiated Mask Propagation Mechanism: Directly derives object-specific masks from diffusion attention and efficiently propagates them across frames.
- �� Experimental Design: Tested on 200 video-image pairs to verify the method's effectiveness.
Experiments
Experiments were conducted on multiple datasets, including FlexiAct, Pexels, DAVIS 2017, and Seedream, covering 20 different types of motion. Baselines include FlexiAct, I2VEdit, AnyV2V, and others. Evaluation metrics include appearance consistency, temporal consistency, text similarity, and motion fidelity.
Results
FlexiMMT improved motion fidelity by 23% and excelled in appearance and temporal consistency. Ablation studies confirmed the critical role of Motion Decoupled Mask Attention in motion assignment.
Applications
FlexiMMT has broad applications in film production, animation, and other fields, especially in scenarios requiring precise motion control.
Limitations & Outlook
While FlexiMMT has made significant progress in multi-object multi-motion transfer, there is room for improvement in handling complex backgrounds or fast motion. Future research could explore more efficient mask generation methods and applications in real-time video processing.
Plain Language Accessible to non-experts
Imagine you're playing a puzzle game where each piece represents an object's motion. FlexiMMT is like a smart assistant that helps you find the right pieces from different boxes and place them in the correct spots. This way, each object can move as you want without interfering with each other. This assistant is not only fast but also ensures each piece fits perfectly.
ELI14 Explained like you're 14
Imagine you're playing a super cool game where you can make the characters in a picture move! FlexiMMT is like a magic tool that learns different actions from various videos and then gives these actions to the characters in the picture. For example, you can make a tiger walk on snow, a rabbit stand up, or even a cat do yoga! Isn't that amazing? However, sometimes it might have a little trouble with complex backgrounds or fast actions, but overall, it's really awesome!
Glossary
Motion Decoupled Mask Attention
A mechanism using object-specific masks to constrain attention, ensuring motion and text tokens only affect designated regions.
Used to solve cross-object motion entanglement.
Differentiated Mask Propagation Mechanism
A mechanism that directly derives object-specific masks from diffusion attention and efficiently propagates them across frames.
Used for generating and propagating object-specific masks.
Implicit Motion Representation
Motion-related vectors, attention maps, or parameters extracted from reference videos for motion capture and transfer.
Used to avoid explicit motion preprocessing.
Appearance Consistency
Evaluates the similarity in appearance between the generated video and the input image.
One of the metrics for assessing video generation quality.
Motion Fidelity
Evaluates the similarity of motion between the generated video and the reference video.
A metric for assessing motion transfer accuracy.
Open Questions Unanswered questions from this research
- 1 How to improve FlexiMMT's performance in complex backgrounds or fast motion scenarios?
- 2 Can more efficient mask generation methods be developed to enhance real-time processing capabilities?
Applications
Immediate Applications
Film Production
FlexiMMT can be used in film and animation production to achieve precise motion control of multiple objects, enhancing visual effects.
Long-term Vision
Real-Time Video Processing
In the future, it can be applied to real-time video processing, supporting more complex scenarios and more efficient motion transfer.
Abstract
Motion transfer has emerged as a promising direction for controllable video generation, yet existing methods largely focus on single-object scenarios and struggle when multiple objects require distinct motion patterns. In this work, we present FlexiMMT, the first implicit image-to-video (I2V) motion transfer framework that explicitly enables multi-object, multi-motion transfer. Given a static multi-object image and multiple reference videos, FlexiMMT independently extracts motion representations and accurately assigns them to different objects, supporting flexible recombination and arbitrary motion-to-object mappings. To address the core challenge of cross-object motion entanglement, we introduce a Motion Decoupled Mask Attention Mechanism that uses object-specific masks to constrain attention, ensuring that motion and text tokens only influence their designated regions. We further propose a Differentiated Mask Propagation Mechanism that derives object-specific masks directly from diffusion attention and progressively propagates them across frames efficiently. Extensive experiments demonstrate that FlexiMMT achieves precise, compositional, and state-of-the-art performance in I2V-based multi-object multi-motion transfer. Our project page is: https://ethan-li123.github.io/FlexiMMT_page/