CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for Zero-Shot Customized Video Diffusion Transformers (2502.06527v2)

Abstract: Customized generation has achieved significant progress in image synthesis, yet personalized video generation remains challenging due to temporal inconsistencies and quality degradation. In this paper, we introduce CustomVideoX, an innovative framework leveraging the video diffusion transformer for personalized video generation from a reference image. CustomVideoX capitalizes on pre-trained video networks by exclusively training the LoRA parameters to extract reference features, ensuring both efficiency and adaptability. To facilitate seamless interaction between the reference image and video content, we propose 3D Reference Attention, which enables direct and simultaneous engagement of reference image features with all video frames across spatial and temporal dimensions. To mitigate the excessive influence of reference image features and textual guidance on generated video content during inference, we implement the Time-Aware Reference Attention Bias (TAB) strategy, dynamically modulating reference bias over different time steps. Additionally, we introduce the Entity Region-Aware Enhancement (ERAE) module, aligning highly activated regions of key entity tokens with reference feature injection by adjusting attention bias. To thoroughly evaluate personalized video generation, we establish a new benchmark, VideoBench, comprising over 50 objects and 100 prompts for extensive assessment. Experimental results show that CustomVideoX significantly outperforms existing methods in terms of video consistency and quality.

• The paper introduces a zero-shot video generation framework that leverages minimal LoRA adaptation to integrate 3D reference attention without retraining.
• It employs a time-aware reference attention bias to dynamically balance structural preservation and motion detail during diffusion denoising.
• Experimental results on VideoBench demonstrate superior temporal coherence and identity preservation compared to previous video synthesis methods.

An In-Depth Analysis of "CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for Zero-Shot Customized Video Diffusion Transformers"

The paper introduces CustomVideoX, a framework designed to advance the capabilities of zero-shot personalized video generation using video diffusion transformers (VDiT). It addresses the challenges of integrating reference images into video generation while maintaining both temporal consistency and detail fidelity. This paper is relevant for researchers interested in video synthesis, particularly those focusing on fine-tuning diffusion models for customized content generation.

Overview of CustomVideoX

CustomVideoX presents a sophisticated method for video generation that leverages a pre-trained video diffusion model while dynamically incorporating additional information from reference images. The method involves minimal modification to the existing model through the use of LoRA (Low-Rank Adaptation) parameters, thereby enabling efficient feature extraction from input reference images. This approach allows the framework to eschew the need for retraining while maintaining adaptability and efficiency.

Key Innovations

1. 3D Reference Attention: This mechanism allows seamless interaction between the reference image and video content by engaging image features across all frames in spatial and temporal dimensions simultaneously. The integration ensures that every frame can directly relate to the reference content, bypassing the need for separate temporal-spatial attention stages.
2. Time-Aware Reference Attention Bias (TAB): TAB is introduced to dynamically modulate the influence of reference features over time during the denoising process, inherent in diffusion models. At different time steps, the bias applied is adjusted to favor structural preservation in early stages, and dynamic motion features in later stages.
3. Entity Region-Aware Enhancement (ERAE): ERAE aligns significant regions of key entity tokens with reference feature injection, ensuring that attention is directed towards critical areas of the generated content, thereby enhancing identity preservation and detail consistency across frames.

Evaluation and Results

The paper established a benchmark named VideoBench to effectively evaluate the proposed method. This benchmark includes over 50 object categories and more than 100 prompts, facilitating a rigorous assessment of model performance in text-to-video tasks. Experimental results confirm that CustomVideoX outperforms existing methods in maintaining both video quality and thematic consistency. Notably, it achieves superior performance in terms of temporal coherence and subject fidelity.

Implications and Future Directions

CustomVideoX signifies substantial progress in the field of automated video generation, particularly in contexts where video resources are limited. By using reference images to aid in video creation without extensive model retraining, CustomVideoX provides a scalable approach applicable to various real-world scenarios such as digital content creation and advertisement customization.

The integration of 3D reference attention mechanisms within diffusion transformers represents an exciting frontier for future research. Investigations into further reducing computational overhead while increasing model flexibility could lead to more robust individualized content generation systems. As diffusion models continue to mature, their application in domains requiring temporal consistency and detail accuracy in video generation will likely expand, paving the way for richer, more adaptive machine learning models.

Conclusion

CustomVideoX offers a nuanced advancement in zero-shot video generation by integrating reference features intelligently within the video diffusion transformer framework. This research exemplifies how leveraging attention mechanisms can significantly enhance personalized video synthesis, reinforcing the utility of diffusion models in dynamic content creation. As such, CustomVideoX provides a promising direction for future work in highly personalized and context-aware video generation systems.