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CCEdit: Versatile Generative Video Editing with Precise Control over Structure and Appearance


핵심 개념
CCEdit is a versatile generative video editing framework that decouples structure and appearance control, enabling precise and creative editing capabilities through a novel trident network architecture.
요약
The paper presents CCEdit, a generative video editing framework that aims to strike a balance between controllability and creativity. The key aspects of the approach are: Trident Network Architecture: The framework comprises three main components - a main text-to-video generation branch, a structure control branch, and an appearance control branch. The structure branch, implemented as ControlNet, digests structural information (e.g., line drawings, depth maps) from the input video and seamlessly integrates it into the main branch. The appearance branch introduces a mechanism for precise appearance control by allowing the integration of an edited reference frame. The three branches are effectively combined through learnable temporal layers to ensure temporal consistency across the generated video frames. Versatile Control Options: Users can choose from various types of structural information (line drawings, depth maps, etc.) as input to the structure branch. Personalized text-to-image models from the Stable Diffusion community can be integrated as plugins, offering greater flexibility and creativity. The appearance branch can accommodate an edited reference frame, facilitating fine-grained appearance control. BalanceCC Benchmark: To address the challenges in evaluating generative video editing approaches, the authors introduce the BalanceCC benchmark dataset. The dataset comprises 100 diverse videos with detailed scene descriptions and attributes, generated with the assistance of the GPT-4V(ision) model. The benchmark serves as a comprehensive evaluation platform for the dynamic field of generative video editing. Experimental Evaluation: Extensive user studies compare CCEdit with eight state-of-the-art video editing methods, demonstrating CCEdit's substantial superiority over all other methods. Qualitative results showcase CCEdit's ability to achieve precise editing objectives while maintaining temporal coherence and structural integrity. The paper highlights CCEdit's versatility, controllability, and creativity in the domain of generative video editing, making it a compelling choice for AI-assisted video editing workflows.
통계
"In recent years, the domain of visual content creation and editing has undergone a profound transformation, driven by the emergence of diffusion-based generative models." "Diverse editing requirements include tasks such as stylistic alterations, foreground replacements, and background modifications." "CCEdit achieves its goal by effectively decoupling structure and appearance control in a unified trident network." "The versatility of our framework is demonstrated through a diverse range of choices in both structure representations and personalized T2I models, as well as the option to provide the edited key frame." "To address the challenges inherent in evaluating generative video editing methods, we introduce the BalanceCC benchmark dataset."
인용구
"CCEdit achieves its goal by effectively decoupling structure and appearance control in a unified trident network." "The versatility of our framework is demonstrated through a diverse range of choices in both structure representations and personalized T2I models, as well as the option to provide the edited key frame." "To address the challenges inherent in evaluating generative video editing methods, we introduce the BalanceCC benchmark dataset."

에서 추출된 핵심 인사이트

by Ruoyu Feng,W... 에서 arxiv.org 04-09-2024

https://arxiv.org/pdf/2309.16496.pdf
CCEdit

더 깊은 문의

How can the structure control branch of CCEdit be further improved to handle more complex structural transformations, such as converting a "cute rabbit" into a "majestic tiger"?

In order to enhance the structure control branch of CCEdit for handling intricate structural transformations like changing a "cute rabbit" into a "majestic tiger," several improvements can be considered: Advanced Structural Representations: Introduce more sophisticated structural representations beyond line drawings, PiDi boundaries, and depth maps. Incorporating advanced techniques like shape descriptors, semantic segmentation masks, or even 3D skeletal structures can provide a richer source of information for the model to work with. Hierarchical Structural Guidance: Implement a hierarchical approach where the model can understand and manipulate structures at different levels of abstraction. This can enable the transformation of complex shapes and features with greater precision. Adaptive Structural Learning: Develop mechanisms for the model to adapt and learn from the input data during training. This adaptive learning can help the model better understand and manipulate diverse structural elements, making it more versatile in handling complex transformations. Fine-tuning and Transfer Learning: Incorporate fine-tuning and transfer learning techniques to allow the model to specialize in specific types of structural transformations. By training on a diverse set of examples, the model can learn to handle a wide range of structural changes effectively. Feedback Mechanisms: Implement feedback loops where the model can receive input or corrections from users during the editing process. This interactive approach can help refine the structural transformations and ensure the desired output is achieved accurately.

What are the potential limitations or drawbacks of relying on pre-trained text-to-image models as the foundation for the main branch of CCEdit, and how could these be addressed?

While using pre-trained text-to-image models as the foundation for the main branch of CCEdit offers several advantages, there are potential limitations and drawbacks to consider: Domain Specificity: Pre-trained models may be biased towards the data they were trained on, limiting their ability to generalize to diverse editing tasks. This can result in suboptimal performance when applied to new or unseen scenarios. Lack of Flexibility: Pre-trained models may have fixed architectures and features, restricting the flexibility to adapt to different editing requirements. This rigidity can hinder the model's ability to handle complex transformations effectively. Overfitting: There is a risk of overfitting to the training data, especially if the pre-trained model is not fine-tuned or adapted to the specific task of video editing. This can lead to poor generalization and limited creativity in the editing process. Limited Control: Pre-trained models may not provide fine-grained control over the editing process, particularly when it comes to intricate appearance modifications or style transfers. This lack of control can restrict the creative possibilities for users. To address these limitations, the following strategies can be implemented: Fine-tuning: Fine-tune the pre-trained models on a diverse set of video editing tasks to adapt them to the specific requirements of CCEdit. This can help improve performance and generalization. Ensemble Models: Combine multiple pre-trained models or architectures to leverage their individual strengths and mitigate their weaknesses. Ensemble learning can enhance the overall performance and robustness of the system. Continual Learning: Implement continual learning techniques to allow the model to adapt and improve over time as it encounters new editing tasks. This can help overcome limitations related to domain specificity and flexibility. User Feedback Mechanisms: Incorporate mechanisms for users to provide feedback on the editing results, enabling the model to learn from user interactions and improve its performance based on real-world usage.

How might the BalanceCC benchmark dataset be expanded or adapted to better capture the evolving needs and challenges in the field of generative video editing, particularly as new techniques and applications emerge?

To enhance the relevance and effectiveness of the BalanceCC benchmark dataset in capturing the evolving needs and challenges in generative video editing, the following adaptations and expansions can be considered: Incorporating Dynamic Scenes: Include videos with dynamic and interactive scenes that require complex editing techniques such as object manipulation, scene transitions, and motion tracking. This can better reflect real-world editing scenarios and challenges. Multi-Modal Data: Expand the dataset to include multi-modal data sources such as audio, text, and metadata associated with videos. This can enable evaluation of cross-modal editing tasks and foster research in multi-modal generative video editing. Fine-Grained Annotations: Provide detailed annotations for different aspects of videos, including object attributes, scene semantics, and temporal dynamics. Fine-grained annotations can facilitate more nuanced evaluation metrics and analysis of editing performance. Long-Form Videos: Include longer videos with varying durations to assess the model's ability to maintain consistency and quality over extended sequences. Evaluating performance on long-form videos can reveal challenges related to temporal coherence and editing scalability. User-Centric Evaluation: Introduce user-centric evaluation metrics and tasks to gauge the subjective quality and usability of edited videos. Incorporating user feedback and preferences can provide valuable insights into the practical utility of generative video editing methods. Benchmark Updates: Regularly update the benchmark dataset with new video samples, diverse editing tasks, and emerging editing techniques to stay abreast of the latest advancements in the field. This continuous evolution ensures that the benchmark remains relevant and reflective of current trends in generative video editing.
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