MotionCrafter: One-Shot Motion Customization of Diffusion Models (2312.05288v2)

Abstract: The essence of a video lies in its dynamic motions, including character actions, object movements, and camera movements. While text-to-video generative diffusion models have recently advanced in creating diverse contents, controlling specific motions through text prompts remains a significant challenge. A primary issue is the coupling of appearance and motion, often leading to overfitting on appearance. To tackle this challenge, we introduce MotionCrafter, a novel one-shot instance-guided motion customization method. MotionCrafter employs a parallel spatial-temporal architecture that injects the reference motion into the temporal component of the base model, while the spatial module is independently adjusted for character or style control. To enhance the disentanglement of motion and appearance, we propose an innovative dual-branch motion disentanglement approach, comprising a motion disentanglement loss and an appearance prior enhancement strategy. During training, a frozen base model provides appearance normalization, effectively separating appearance from motion and thereby preserving diversity. Comprehensive quantitative and qualitative experiments, along with user preference tests, demonstrate that MotionCrafter can successfully integrate dynamic motions while preserving the coherence and quality of the base model with a wide range of appearance generation capabilities. Project page: https://zyxelsa.github.io/homepage-motioncrafter. Codes are available at https://github.com/zyxElsa/MotionCrafter.

Introduction to MotionCrafter

In an innovative approach to motion generation, a paper introduces "MotionCrafter," a novel method that considerably enhances the capability of diffusion models. This technique serves to customize the generation of motion in a one-shot process. Rather than requiring extensive training or multiple inputs, it enables the model to craft distinct motions from a single example or command.

Core Principles and Methodology

At the heart of MotionCrafter is the utilization of diffusion models, which are a class of machine learning models that learn to generate data by gradually adding structured noise and then learning to reverse this process. The authors present a method that infuses user-defined constraints into the motion generation process. These constraints might involve defining the starting pose of a figure, the trajectory of a walk, or the ending stance of a dance move. Remarkably, the model demonstrates high flexibility in adapting to various motion styles and complexities with minimal input data.

Applications and Implications

This technology has significant implications for industries like animation, video game development, and virtual reality, where bespoke motion generation is essential. It offers a new layer of efficiency and personalization, allowing creators to produce high-quality, tailored animations without needing extensive datasets. With MotionCrafter, a user can quickly iterate on creative ideas, refine movements, and develop animations that would traditionally take extensive time and effort to handcraft.

Future Outlook

The paper concludes with discussions on the implications and potential future directions for MotionCrafter. It touches upon the possibilities for more advanced customizations, including adapting motions to different body types or physical characteristics automatically. Furthermore, expanding on the diffusion model's capabilities could lead to more nuanced control over generated motions and even finer one-shot motion customization. As the field of AI continues to evolve, methods such as MotionCrafter open the door to a future where high-quality motion generation can be made more accessible and user-friendly for creative professionals across a spectrum of industries.