by jsendak | Jul 26, 2024 | Computer Science
arXiv:2407.17911v1 Announce Type: new
Abstract: Diffusion models revolutionize image generation by leveraging natural language to guide the creation of multimedia content. Despite significant advancements in such generative models, challenges persist in depicting detailed human-object interactions, especially regarding pose and object placement accuracy. We introduce a training-free method named Reasoning and Correcting Diffusion (ReCorD) to address these challenges. Our model couples Latent Diffusion Models with Visual Language Models to refine the generation process, ensuring precise depictions of HOIs. We propose an interaction-aware reasoning module to improve the interpretation of the interaction, along with an interaction correcting module to refine the output image for more precise HOI generation delicately. Through a meticulous process of pose selection and object positioning, ReCorD achieves superior fidelity in generated images while efficiently reducing computational requirements. We conduct comprehensive experiments on three benchmarks to demonstrate the significant progress in solving text-to-image generation tasks, showcasing ReCorD’s ability to render complex interactions accurately by outperforming existing methods in HOI classification score, as well as FID and Verb CLIP-Score. Project website is available at https://alberthkyhky.github.io/ReCorD/ .
Analysis: Reasoning and Correcting Diffusion (ReCorD) in Multimedia Image Generation
In the field of multimedia information systems, the generation of realistic and detailed images has been an ongoing challenge. This is particularly true when it comes to human-object interactions (HOIs), where accurately depicting the pose and placement of objects in relation to humans is crucial for creating immersive and authentic visuals.
However, recent advancements in generative models, especially those leveraging natural language input, have shown promise in improving image generation. The article introduces a novel training-free method called Reasoning and Correcting Diffusion (ReCorD), which aims to address the challenges in generating accurate HOIs by combining Latent Diffusion Models with Visual Language Models.
One of the key contributions of ReCorD is the incorporation of an interaction-aware reasoning module. By considering the context and semantics of the input text description, this module enhances the understanding of the intended interaction between humans and objects. This is crucial for generating images that accurately depict the desired pose and object placement.
Furthermore, ReCorD also introduces an interaction correcting module, which refines the output image to ensure precision in HOI generation. This fine-tuning process takes into account intricate details of human-object interactions, resulting in images with superior fidelity. Moreover, by carefully selecting poses and positioning objects, ReCorD manages to reduce the computational requirements without compromising the quality of the generated images.
What makes ReCorD particularly interesting is its multi-disciplinary nature. It combines techniques from computer vision, natural language processing, and generative modeling to address the challenges in HOI generation. By integrating these diverse disciplines, ReCorD pushes the boundaries of text-to-image synthesis and demonstrates the potential of combining different approaches to achieve more accurate and realistic images.
In the wider field of multimedia information systems, ReCorD aligns with the research on image generation, which has seen significant progress in recent years. The use of diffusion models and the incorporation of natural language guidance further strengthen the connection to multimedia information systems, as these techniques allow for semantic understanding and context-aware generation of visuals.
In addition, ReCorD’s focus on human-object interactions and accurate depiction of poses and object placements highlights its relevance to animations, artificial reality, augmented reality, and virtual realities. These technologies rely on realistic visuals to create immersive experiences, and ReCorD’s advancements in image generation can potentially enhance the quality and authenticity of such virtual environments.
In conclusion, ReCorD presents an innovative approach to generating images that accurately depict human-object interactions. By leveraging the strengths of diffusion models and visual language models, as well as incorporating reasoning and correcting modules, ReCorD achieves superior fidelity in generated images. The multi-disciplinary nature of ReCorD aligns it with the wider field of multimedia information systems and its relevance to various technologies like animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jul 20, 2024 | AI
arXiv:2407.12899v1 Announce Type: new Abstract: Story visualization aims to create visually compelling images or videos corresponding to textual narratives. Despite recent advances in diffusion models yielding promising results, existing methods still struggle to create a coherent sequence of subject-consistent frames based solely on a story. To this end, we propose DreamStory, an automatic open-domain story visualization framework by leveraging the LLMs and a novel multi-subject consistent diffusion model. DreamStory consists of (1) an LLM acting as a story director and (2) an innovative Multi-Subject consistent Diffusion model (MSD) for generating consistent multi-subject across the images. First, DreamStory employs the LLM to generate descriptive prompts for subjects and scenes aligned with the story, annotating each scene’s subjects for subsequent subject-consistent generation. Second, DreamStory utilizes these detailed subject descriptions to create portraits of the subjects, with these portraits and their corresponding textual information serving as multimodal anchors (guidance). Finally, the MSD uses these multimodal anchors to generate story scenes with consistent multi-subject. Specifically, the MSD includes Masked Mutual Self-Attention (MMSA) and Masked Mutual Cross-Attention (MMCA) modules. MMSA and MMCA modules ensure appearance and semantic consistency with reference images and text, respectively. Both modules employ masking mechanisms to prevent subject blending. To validate our approach and promote progress in story visualization, we established a benchmark, DS-500, which can assess the overall performance of the story visualization framework, subject-identification accuracy, and the consistency of the generation model. Extensive experiments validate the effectiveness of DreamStory in both subjective and objective evaluations. Please visit our project homepage at https://dream-xyz.github.io/dreamstory.
The article “DreamStory: An Automatic Open-Domain Story Visualization Framework” introduces a novel framework called DreamStory that aims to create visually compelling images or videos based on textual narratives. While existing methods have made progress, they still struggle to generate a coherent sequence of subject-consistent frames solely from a story. DreamStory addresses this challenge by leveraging Language and Vision models (LLMs) and a Multi-Subject consistent Diffusion model (MSD). The framework consists of an LLM that acts as a story director and an MSD that generates consistent multi-subjects across the images. DreamStory uses the LLM to generate descriptive prompts for subjects and scenes, annotating each scene’s subjects for subsequent subject-consistent generation. It then utilizes these detailed subject descriptions to create portraits of the subjects, which serve as multimodal anchors. The MSD employs Masked Mutual Self-Attention and Masked Mutual Cross-Attention modules to ensure appearance and semantic consistency with reference images and text. Experiments validate the effectiveness of DreamStory, and a benchmark, DS-500, is established to assess the overall performance of the framework.
Exploring DreamStory: A New Approach to Story Visualization
The field of story visualization has made significant progress in recent years, with researchers striving to create visually striking images and videos that accurately represent textual narratives. While diffusion models have shown promise, existing methods still struggle to seamlessly create a coherent sequence of subject-consistent frames based solely on a story. In response to this challenge, DreamStory offers an innovative solution, introducing an automatic open-domain story visualization framework that leverages Language and Vision Models (LLMs) along with a novel multi-subject consistent diffusion model.
The Components of DreamStory
DreamStory comprises two key components:
- LLM as the Story Director: The LLM plays a crucial role in DreamStory by generating descriptive prompts aligned with the story. These prompts help create subjects and scenes that are coherent with the narrative, ensuring subject-consistent generation in subsequent stages.
- Multi-Subject Consistent Diffusion Model (MSD): The MSD, an innovative addition to DreamStory, utilizes detailed subject descriptions provided by the LLM to generate portraits of the subjects. These portraits, along with corresponding textual information, serve as multimodal anchors or guidance. The MSD uses these anchors to generate story scenes with consistent multi-subject representation.
Incorporating this multi-subject consistency is crucial in story visualization, as it enhances the overall coherence and immersion experienced by the viewer. Without subject consistency, the visual representation may become disjointed or confusing, hindering the storytelling aspect of the visualization.
Making Use of Masked Mutual Self-Attention and Cross-Attention
The MSD component of DreamStory incorporates Masked Mutual Self-Attention (MMSA) and Masked Mutual Cross-Attention (MMCA) modules to ensure appearance and semantic consistency in the generated visuals.
The MMSA module helps maintain appearance consistency by leveraging reference images. This module ensures that the generated visuals resemble the reference images in terms of appearance, preventing any abrupt changes or discrepancies. Consequently, the generated frames smoothly transition while maintaining visual cohesiveness.
The MMCA module, on the other hand, focuses on semantic consistency with reference texts. By incorporating the textual information provided alongside the subject portraits, DreamStory ensures that the generated visuals adhere to the intended semantic context. This module ensures that the visuals accurately represent the textual descriptions, enriching the viewer’s understanding of the story.
Both modules employ masking mechanisms that prevent subject blending. This approach ensures that each subject retains its distinct characteristics and does not overlap with other subjects, achieving a visually pleasing and coherent composition.
Evaluating DreamStory’s Performance
In order to validate DreamStory’s effectiveness and encourage further advancements in story visualization, a benchmark called DS-500 has been established. This benchmark assesses DreamStory’s overall performance, subject-identification accuracy, and the consistency of the generation model.
Extensive experiments have been conducted to evaluate DreamStory using both subjective and objective measures. The results have demonstrated the efficacy of DreamStory in creating visually engaging and subject-consistent story visualizations. These findings contribute to the ongoing progress in the field and pave the way for future innovations in story visualization techniques.
If you are interested in learning more about DreamStory and exploring its capabilities, please visit our project homepage at https://dream-xyz.github.io/dreamstory.
The paper titled “DreamStory: An Automatic Open-Domain Story Visualization Framework” introduces a novel approach to story visualization using language models and a multi-subject consistent diffusion model. Story visualization involves generating visually appealing images or videos that correspond to textual narratives. While there have been advancements in diffusion models for this task, existing methods struggle to create a coherent sequence of subject-consistent frames solely based on a story.
The proposed framework, DreamStory, consists of two main components: an LLM (Language and Logic Model) acting as a story director and a Multi-Subject consistent Diffusion model (MSD) for generating consistent multi-subject across images. The LLM is responsible for generating descriptive prompts for subjects and scenes aligned with the story. It annotates each scene’s subjects, enabling subsequent subject-consistent generation. This step helps establish a strong foundation for the visual representation of the story.
DreamStory leverages detailed subject descriptions generated by the LLM to create portraits of the subjects. These portraits, along with their corresponding textual information, serve as multimodal anchors or guidance for the generation process. The MSD utilizes these multimodal anchors to generate story scenes with consistent multi-subject. The MSD includes Masked Mutual Self-Attention (MMSA) and Masked Mutual Cross-Attention (MMCA) modules to ensure appearance and semantic consistency with reference images and text, respectively. The masking mechanisms employed by these modules prevent subject blending, resulting in more coherent and visually consistent story visualizations.
To evaluate the proposed approach and facilitate further research in story visualization, the authors have introduced a benchmark called DS-500. This benchmark assesses the overall performance of the story visualization framework, subject-identification accuracy, and the consistency of the generation model. The authors have conducted extensive experiments to validate the effectiveness of DreamStory, using both subjective and objective evaluations.
Overall, this paper presents a promising approach to open-domain story visualization by combining language models and a multi-subject consistent diffusion model. The use of multimodal anchors and the incorporation of masking mechanisms in the MSD contribute to generating visually coherent and subject-consistent story scenes. The establishment of a benchmark for evaluation purposes is a valuable contribution to the field, enabling researchers to compare and improve upon existing methods. This work opens up possibilities for further advancements in story visualization and has the potential to enhance various applications such as movie production, video game design, and virtual reality experiences.
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by jsendak | Jul 11, 2024 | Computer Science
arXiv:2407.07111v1 Announce Type: cross
Abstract: The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making “what you want is what you see” a reality. Among these, video editing has gained substantial attention and seen a swift rise in research activity, necessitating a comprehensive and systematic review of the existing literature. This paper reviews diffusion model-based video editing techniques, including theoretical foundations and practical applications. We begin by overviewing the mathematical formulation and image domain’s key methods. Subsequently, we categorize video editing approaches by the inherent connections of their core technologies, depicting evolutionary trajectory. This paper also dives into novel applications, including point-based editing and pose-guided human video editing. Additionally, we present a comprehensive comparison using our newly introduced V2VBench. Building on the progress achieved to date, the paper concludes with ongoing challenges and potential directions for future research.
Expert Commentary: Advances in Diffusion Model-Based Video Editing Techniques
Video editing has become a crucial component in the multimedia information systems field, enabling users to create visually appealing and informative content. The rapid development of diffusion models (DMs) has significantly enhanced the capabilities of image and video applications, allowing users to see exactly what they want. This paper provides a comprehensive and systematic review of the existing literature on diffusion model-based video editing techniques, shedding light on their theoretical foundations, practical applications, and future directions for research.
One of the key strengths of this paper is its multi-disciplinary nature. Video editing techniques in diffusion models draw upon concepts from various fields such as computer vision, image processing, and machine learning. By exploring the mathematical formulation and key methods in the image domain, the paper establishes the theoretical foundations of diffusion model-based video editing techniques. This interdisciplinary approach is crucial for understanding the complex algorithms underlying these techniques and their potential applications.
The paper categorizes video editing approaches based on the inherent connections of their core technologies, providing a comprehensive overview of the evolutionary trajectory in this field. This categorization aids in understanding the different techniques employed and their relative strengths and limitations. Furthermore, the paper goes beyond traditional video editing techniques and explores novel applications such as point-based editing and pose-guided human video editing. These innovative applications demonstrate the versatility of diffusion model-based video editing techniques and their potential impact on various domains, including entertainment, advertising, and education.
In addition, the paper introduces V2VBench, a comprehensive comparison framework that allows for a quantitative evaluation of different diffusion model-based video editing techniques. This framework enables researchers and practitioners to objectively assess the performance of these techniques, facilitating benchmarking and further advancements in the field.
When considering the wider field of multimedia information systems, diffusion model-based video editing techniques play a significant role in enhancing the user experience. These techniques contribute to the creation of visually stunning animations, artificial realities, augmented realities, and virtual realities. By utilizing diffusion models, video editors can manipulate videos in a way that seamlessly integrates with these multimedia systems. This integration opens up new avenues for immersive storytelling, interactive experiences, and realistic simulations.
However, despite the progress achieved so far, several challenges remain in diffusion model-based video editing. These include improving the efficiency and scalability of existing algorithms, developing techniques for handling complex video scenes, and addressing the ethical considerations surrounding the manipulation of video content. These challenges present exciting opportunities for future research, as they push the boundaries of current techniques and pave the way for innovative solutions.
In conclusion, this paper provides a comprehensive review of diffusion model-based video editing techniques, highlighting their theoretical foundations, practical applications, and future directions. With its multi-disciplinary approach and emphasis on novel applications, the paper significantly contributes to the wider field of multimedia information systems, making it a valuable resource for researchers, practitioners, and enthusiasts in this field.
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by jsendak | Jul 7, 2024 | AI
As diffusion models are deployed in real-world settings, data attribution is needed to ensure fair acknowledgment for contributors of high-quality training data and to identify sources of harmful…
As the deployment of diffusion models in real-world applications becomes increasingly prevalent, the issue of data attribution has come to the forefront. It is crucial to establish mechanisms that ensure fair acknowledgment for the contributors of high-quality training data while also identifying the sources of harmful or biased information. This article delves into the core themes surrounding data attribution, highlighting the importance of recognizing and crediting those who contribute to the development of these models. Additionally, it explores the need to identify and address potential sources of harmful data, emphasizing the significance of fair and responsible use of diffusion models in various contexts.
As diffusion models continue to gain traction in real-world applications, it becomes increasingly important to address the issue of data attribution and fair acknowledgment for contributors of high-quality training data. Additionally, there is a pressing need to identify and address sources of harmful or biased data that can potentially undermine the integrity of these models. By exploring these underlying themes and proposing innovative solutions, we can pave the way for a more ethical and responsible use of diffusion models.
The Importance of Data Attribution
Data attribution refers to the process of recognizing and acknowledging the individuals or organizations that contribute to the creation and curation of training data used in diffusion models. This attribution is crucial for several reasons:
- Recognition: By attributing the data contributors, we can provide them with the recognition they deserve for their valuable contributions. This recognition can motivate individuals and organizations to continue providing high-quality training data.
- Accountability: Attribution holds contributors accountable for the data they provide. If a particular contributor consistently provides biased or harmful data, their attribution can help identify the source of the problem.
- Transparency: Data attribution promotes transparency by allowing researchers and users of diffusion models to understand the origin and quality of the training data. This transparency is crucial for establishing trust in these models.
Addressing Harmful and Biased Data
Diffusion models are only as good as the data they are trained on. It is imperative to identify and address sources of harmful or biased data to ensure the integrity and fairness of these models. Here are a few ideas to tackle this issue:
- Data Quality Assessment: Implement rigorous and comprehensive assessment methods to evaluate the quality of training data. This can involve manual review, automated checks, and third-party audits.
- Diverse Data Sources: Ensure that the training data comes from diverse sources representing various demographics, cultures, and perspectives. This can help mitigate biases and avoid over-representation of certain groups.
- Community Review: Encourage a community-driven approach where researchers and users actively engage in identifying and reporting instances of harmful or biased data. This can help create a collective responsibility for addressing these issues.
- Ethics Guidelines: Establish clear and enforceable ethics guidelines for data collection, annotation, and usage in diffusion models. These guidelines should emphasize fairness, inclusivity, and the avoidance of harm or discrimination.
Innovative Solutions for Data Attribution
To address the issue of data attribution in diffusion models, we should explore innovative solutions that leverage technology and collaboration. Here are a few ideas:
- Blockchain-Based Attribution: Utilize blockchain technology to create a decentralized and immutable record of data contributions. This can ensure secure and transparent attribution while maintaining privacy.
- Data Contributor Identifiers: Introduce unique identifiers for data contributors that can be embedded within the model architecture. These identifiers can be used to automatically attribute the contributions of individual data providers.
- Crowdsourced Attribution: Tap into the power of crowdsourcing by involving a wider community in the attribution process. This can help distribute the responsibility and prevent undue reliance on a single authority for attribution.
By prioritizing data attribution and addressing issues of harmful and biased data, we can build diffusion models that are not only technically advanced but also ethically responsible. It is crucial for researchers, practitioners, and policymakers to collaborate and innovate in this domain to ensure a fair and inclusive future for diffusion models.
bias or misinformation. Data attribution refers to the process of giving credit to individuals or organizations for their contributions to the training data used in diffusion models. This is crucial to ensure transparency, accountability, and fairness in the development and deployment of these models.
One of the main challenges with data attribution is the complexity of tracking and identifying the sources of training data. Diffusion models often rely on vast amounts of data from various sources, including publicly available information, licensed datasets, and user-generated content. Attribution becomes particularly challenging when data is aggregated, anonymized, or obtained through third-party providers.
To address these challenges, organizations developing diffusion models need to establish robust data governance frameworks. These frameworks should include mechanisms for tracking the origin and ownership of training data, ensuring proper documentation and metadata collection, and implementing clear guidelines for data attribution.
Furthermore, data attribution is not only about giving credit to contributors but also about identifying potential sources of bias or misinformation. Diffusion models can inadvertently amplify and propagate biases present in the training data, leading to unfair or discriminatory outcomes. By properly attributing the data, it becomes easier to identify problematic sources and take corrective actions to mitigate bias.
In the future, we can expect to see advancements in data attribution techniques and technologies. This may involve the development of standardized protocols or metadata formats specifically designed for tracking data contributions in diffusion models. Additionally, leveraging machine learning algorithms and natural language processing techniques could help automate the attribution process, making it more efficient and accurate.
Moreover, as the ethical and societal implications of diffusion models become more apparent, regulatory frameworks might emerge to address data attribution and ensure responsible deployment. These frameworks could require organizations to disclose the sources of training data, undergo third-party audits, or establish independent oversight committees to monitor and assess the impact of diffusion models.
Overall, data attribution is a critical aspect of deploying diffusion models ethically and responsibly. It not only acknowledges the contributions of those who provide high-quality training data but also helps identify and address sources of bias or misinformation. As the field progresses, we can expect to see advancements in data attribution techniques and increased focus on transparency and accountability in the development and deployment of diffusion models.
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by jsendak | Jun 3, 2024 | AI
arXiv:2405.20380v1 Announce Type: new
Abstract: Diffusion models are becoming defector generative models, which generate exceptionally high-resolution image data. Training effective diffusion models require massive real data, which is privately owned by distributed parties. Each data party can collaboratively train diffusion models in a federated learning manner by sharing gradients instead of the raw data. In this paper, we study the privacy leakage risk of gradient inversion attacks. First, we design a two-phase fusion optimization, GIDM, to leverage the well-trained generative model itself as prior knowledge to constrain the inversion search (latent) space, followed by pixel-wise fine-tuning. GIDM is shown to be able to reconstruct images almost identical to the original ones. Considering a more privacy-preserving training scenario, we then argue that locally initialized private training noise $epsilon$ and sampling step t may raise additional challenges for the inversion attack. To solve this, we propose a triple-optimization GIDM+ that coordinates the optimization of the unknown data, $epsilon$ and $t$. Our extensive evaluation results demonstrate the vulnerability of sharing gradient for data protection of diffusion models, even high-resolution images can be reconstructed with high quality.
Analysis of the Content
In this article, the authors discuss the privacy leakage risk of gradient inversion attacks in the context of training diffusion models. Diffusion models are evolving into highly effective generative models that can generate high-resolution image data. However, training these models requires a large amount of real data, which is typically privately owned by distributed parties. To address this, the authors propose a federated learning approach where each data party shares gradients instead of raw data to collaboratively train the diffusion models.
The authors introduce a two-phase fusion optimization method called GIDM (Gradient Inversion Defense Mechanism) to mitigate the privacy leakage risk. GIDM leverages the well-trained generative model itself as prior knowledge to constrain the inversion search space and then performs pixel-wise fine-tuning. The results show that GIDM is able to reconstruct images that are almost identical to the original ones.
Next, the authors consider a more privacy-preserving training scenario and argue that locally initialized private training noise (denoted as $epsilon$) and sampling step (denoted as t) may introduce additional challenges for the inversion attack. To address this, they propose a triple-optimization method called GIDM+ that coordinates the optimization of the unknown data, $epsilon$, and t. The evaluation results demonstrate the vulnerability of sharing gradients for data protection of diffusion models, as high-resolution images can be reconstructed with high quality.
Expert Insights and Multi-disciplinary Nature
This article touches upon several aspects that require multi-disciplinary expertise. The concept of diffusion models as generative models highlights the advancements in the field of computer vision and machine learning. The authors discuss the challenges of training these models using privately owned data and propose a federated learning approach as a solution. This involves the intersection of privacy, distributed computing, and machine learning.
The authors also introduce the concept of gradient inversion attacks and the privacy leakage risks associated with them. This brings in the domain of cybersecurity and adversarial attacks. By analyzing the vulnerabilities and proposing defense mechanisms such as GIDM and GIDM+, the authors contribute to the field of privacy-preserving machine learning and data protection.
The evaluation results presented in the article demonstrate the practical implications of the privacy leakage risks. The ability to reconstruct high-resolution images from shared gradients raises concerns about the privacy of sensitive data. This has implications not only in the field of machine learning but also in domains where privacy is of utmost importance, such as healthcare and finance.
In conclusion, this article highlights the multi-disciplinary nature of the concepts discussed, ranging from computer vision and machine learning to cybersecurity and privacy. The findings and proposed defense mechanisms provide valuable insights for researchers and practitioners working in these fields.
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