Motion Modes: What Could Happen Next? (2412.00148v1)

Abstract: Predicting diverse object motions from a single static image remains challenging, as current video generation models often entangle object movement with camera motion and other scene changes. While recent methods can predict specific motions from motion arrow input, they rely on synthetic data and predefined motions, limiting their application to complex scenes. We introduce Motion Modes, a training-free approach that explores a pre-trained image-to-video generator's latent distribution to discover various distinct and plausible motions focused on selected objects in static images. We achieve this by employing a flow generator guided by energy functions designed to disentangle object and camera motion. Additionally, we use an energy inspired by particle guidance to diversify the generated motions, without requiring explicit training data. Experimental results demonstrate that Motion Modes generates realistic and varied object animations, surpassing previous methods and even human predictions regarding plausibility and diversity. Project Webpage: https://motionmodes.github.io/

• The paper introduces Motion Modes, a training-free framework that leverages pre-trained models and tailored guidance energies to generate diverse and realistic object motions from static images.
• It decouples object movement from camera dynamics through specialized static and object motion guidance, ensuring focused and fluid motion outputs.
• Robust evaluations against baselines highlight its superior diversity and temporal coherence, paving the way for advanced applications in creative video generation.

An Expert Analysis of "Motion Modes: What Could Happen Next?"

The paper "Motion Modes: What Could Happen Next?" presents Motion Modes, a novel approach to generating diverse object motions from static images. This research addresses a significant challenge in video generation: isolating object movements from other scene dynamics such as camera motions and static environment changes. The authors introduce a method that is both innovative and distinctively useful due to its training-free nature, leveraging a pre-trained image-to-video generator and refining its latent distribution to discover unique, plausible motion trajectories.

Core Contributions

The primary contribution of this paper is the Motion Modes framework. This method fundamentally differentiates itself by being training-free, meaning it can operate directly on pre-trained models without retraining. The authors manage this through a flow generator, using carefully tailored guidance energies to explore the motion space. The methodology elegantly separates object motion from camera motion and other scene alterations, thus simplifying the complexity of real-world scene dynamics.

Key components of the Motion Modes framework include:

1. Static Camera Guidance: By penalizing extraneous motion outside the object region, this energy encourages static camera behavior, thus preserving the scene's context.
2. Object Motion Guidance: This component actively promotes motion within the object region, ensuring that identified movements are substantial and meaningful.
3. Diversity Guidance: A crucial innovation, this energy fosters the generation of distinct motion modes by using a repulsive energy term to discourage repetitive motion patterns across samples.
4. Smoothness Guidance: By promoting temporally coherent motion, this aspect of the framework ensures that generated movements are fluid and physically plausible.

Results and Evaluation Metrics

The effectiveness of Motion Modes is validated through a robust set of evaluations against several baselines. Specifically, it outperforms methods such as ControlNet, Prompt-based generation, Random Arrows, and Random/FPS sampling in generating diverse and focused sets of motion outcomes. Quantitatively, the framework was assessed on metrics of diversity (average diversity energy) and focus (average object motion and static camera guidance energies), achieving superior performance that aligns with user expectations of plausible and varied motions.

Participant feedback from user studies highlights that Motion Modes reliably generates expected and plausible motions while providing novel, inspirational motion pathways not initially envisaged by viewers. These outcomes underline the effectiveness of the guided sampling strategy employed.

Implications and Future Directions

From a practical standpoint, Motion Modes presents significant advantages for creative industries, enabling artists and creators to explore potential object motions efficiently without the overhead of manually sieving through large datasets of generated videos. Its application in facilitating interactions with drag-controlled image editors further illustrates its utility in providing detailed and complex motion inputs derived from simple user cues.

The paper opens various avenues for future research. Extending Motion Modes to accommodate scenes with dynamically moving cameras could enhance its applicability in creating sports and action videos, where following moving subjects is critical. Additionally, advancing from 2D to 3D motion predictions could bridge the gap to generating fully animated 3D assets, offering richer, more immersive content creation options.

In summary, the Motion Modes paradigm offers an innovative and practically valuable approach to exploring object motion in static scenes, setting a new standard for leveraging pre-trained models in motion generation and prediction tasks. Its strong performance metrics in generating diverse and expected motion outputs signal a promising direction for future research and applications in AI-driven video generation and editing.