Dynamic Concepts Personalization from Single Videos (2502.14844v1)

Abstract: Personalizing generative text-to-image models has seen remarkable progress, but extending this personalization to text-to-video models presents unique challenges. Unlike static concepts, personalizing text-to-video models has the potential to capture dynamic concepts, i.e., entities defined not only by their appearance but also by their motion. In this paper, we introduce Set-and-Sequence, a novel framework for personalizing Diffusion Transformers (DiTs)-based generative video models with dynamic concepts. Our approach imposes a spatio-temporal weight space within an architecture that does not explicitly separate spatial and temporal features. This is achieved in two key stages. First, we fine-tune Low-Rank Adaptation (LoRA) layers using an unordered set of frames from the video to learn an identity LoRA basis that represents the appearance, free from temporal interference. In the second stage, with the identity LoRAs frozen, we augment their coefficients with Motion Residuals and fine-tune them on the full video sequence, capturing motion dynamics. Our Set-and-Sequence framework results in a spatio-temporal weight space that effectively embeds dynamic concepts into the video model's output domain, enabling unprecedented editability and compositionality while setting a new benchmark for personalizing dynamic concepts.

Dynamic Concepts Personalization from Single Videos: A Professional Overview

The paper "Dynamic Concepts Personalization from Single Videos" addresses the complex task of extending personalization capabilities from text-to-image to text-to-video generative models, which involves capturing dynamic concepts defined by both appearance and motion. This research introduces a novel framework, Set-and-Sequence, to integrate dynamic personalization into Diffusion Transformer (DiT)-based video models, addressing existing impediments in video generation tasks that necessitate spatio-temporal coherence without explicitly separated spatial and temporal features in the model architecture.

Research Methodology

The proposed Set-and-Sequence framework involves two main stages:

1. Identity Basis Learning: The authors fine-tune Low-Rank Adaptation (LoRA) layers using an unordered set of frames from a video to derive an identity LoRA basis. This stage concentrates on capturing the appearance detached from temporal interference, ensuring high-fidelity personalization consistent with static aspects of video elements.
2. Motion Residual Encoding: After identity basis establishment, the framework incorporates motion dynamics by augmenting identity LoRA coefficients with motion residuals. This is achieved by fine-tuning on the full video sequence, thus capturing motion dynamics autonomously, leading to a comprehensive spatio-temporal weight space.

This two-pronged approach results in enhanced editability and compositionality of dynamic concepts, enabling individualized video elements customization and seamless integration of disparate dynamic components, such as blending the motion of ocean waves with the dynamics of a flickering bonfire.

Strong Numerical Results and Implications

The paper sets a new benchmark for personalized dynamic concept integration into video models. Results indicate superior output with significant degrees of freedom in video editing capabilities while maintaining fidelity to original appearance and dynamic attributes. The proposed framework demonstrates high-performance capabilities in video generation, outperforming traditional methods on several compositional and personalization tasks through efficient embedding of spatio-temporal features in weight space.

Practical and Theoretical Implications

From a practical standpoint, this research opens avenues for advanced video editing applications, encompassing tasks that require nuanced manipulation of both static and dynamic video attributes. This could lead to significant improvements in fields such as personalized media content creation, augmented reality, and interactive entertainment experiences.

Theoretically, this work contributes to our understanding of integrating dynamic personalization within generative models, offering a new perspective on disentangling and recomposing temporal and spatial features within video data. It challenges existing paradigms and induces potential for improvements in associated model architectures and training frameworks.

Future Prospects

Looking forward, the framework may be expanded to handle more complex video functionalities, such as real-time adaptations and higher dimensional interactions. Future developments could build on this foundation by incorporating advanced neural architectures that more intricately model temporal elements and extend applications to broader domains, including virtual reality and immersive video gaming.

In conclusion, "Dynamic Concepts Personalization from Single Videos" presents an innovative and methodologically robust approach to personalizing video generation models. It addresses the nuanced challenges of capturing and recomposing dynamic content and offers significant contributions to the development of more intricate and adaptable video generation models.