by jsendak | Apr 8, 2024 | Computer Science
arXiv:2404.04037v1 Announce Type: cross
Abstract: We present InstructHumans, a novel framework for instruction-driven 3D human texture editing. Existing text-based editing methods use Score Distillation Sampling (SDS) to distill guidance from generative models. This work shows that naively using such scores is harmful to editing as they destroy consistency with the source avatar. Instead, we propose an alternate SDS for Editing (SDS-E) that selectively incorporates subterms of SDS across diffusion timesteps. We further enhance SDS-E with spatial smoothness regularization and gradient-based viewpoint sampling to achieve high-quality edits with sharp and high-fidelity detailing. InstructHumans significantly outperforms existing 3D editing methods, consistent with the initial avatar while faithful to the textual instructions. Project page: https://jyzhu.top/instruct-humans .
InstructHumans: Enhancing Instruction-driven 3D Human Texture Editing
In the field of multimedia information systems, the concept of instruction-driven 3D human texture editing plays a crucial role in enhancing the visual quality and realism of virtual characters. This emerging area combines elements from multiple disciplines, including animations, artificial reality, augmented reality, and virtual realities.
The article introduces a novel framework called InstructHumans, which aims to improve the process of instruction-driven 3D human texture editing. It addresses the limitations of existing text-based editing methods that use Score Distillation Sampling (SDS) to distill guidance from generative models. The authors argue that relying solely on these scores can harm the editing process by compromising the consistency with the source avatar.
To overcome this challenge, the researchers propose an alternative approach called Score Distillation Sampling for Editing (SDS-E). This method selectively incorporates subterms of SDS across diffusion timesteps, ensuring that edits maintain consistency with the original avatar. Furthermore, SDS-E is enhanced with spatial smoothness regularization and gradient-based viewpoint sampling to achieve high-quality edits with sharp and high-fidelity detailing.
The results of the study demonstrate that InstructHumans outperforms existing 3D editing methods in terms of preserving consistency with the source avatar while faithfully following the given textual instructions. This advancement in the field of instruction-driven 3D human texture editing paves the way for more immersive and realistic virtual experiences.
The significance of this work extends beyond the specific application of 3D human texture editing. By combining insights from animations, artificial reality, augmented reality, and virtual realities, the researchers contribute to the broader field of multimedia information systems. These interdisciplinary collaborations enable the development of more advanced and sophisticated techniques for creating and manipulating virtual content.
In conclusion, the InstructHumans framework represents a valuable contribution to the field of instruction-driven 3D human texture editing. Its novel approach addresses the limitations of existing methods and demonstrates improved consistency and fidelity in edits. This work demonstrates the importance of interdisciplinary collaboration in advancing the field of multimedia information systems and highlights its relevance to the wider domains of animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 28, 2024 | DS Articles
The hunt is on for better ways to collect and search pandemic studies
Pursuit of Improved Methods for Collecting and Searching Pandemic Studies
The increasing prevalence of COVID-19 has intensified the global quest for more efficient methods of collecting and searching pandemic studies. Given the magnitude and urgency of the situation, scientific research is being produced at an unprecedented rate, exacerbating challenges related to information management, access, and utilisation. This outlines the need for more comprehensive and advanced techniques that would ease the process of collating and probing relevant studies.
Long-Term Implications and Future Developments
Research & Development Evolution
A fundamental shift in the way research and development activities are conducted is to be expected. Apart from digitisation, the scientific community would leverage machine learning and artificial intelligence to automate the process. This could lead to the development of more innovative platforms or databases that are capable of storing vast amounts of data in a well-organised and easily accessible manner.
Policy Formulation and Decision Making
Improved data collection and analysis methods can significantly influence policy formulation, decision-making, and risk management, especially in public health. Having an efficient mechanism to get insights from copious volumes of studies will aid timely, proactive, and evidence-informed responses to future pandemics.
Actionable Recommendations & Insights
Tech Investments and Collaborations
Investment in advanced technologies like AI, machine learning, and blockchain ought to be a primary concern for both public and private institutions aiming to play a significant role in pandemic response era. Collaborations with tech firms and research institutions could greatly speed up the process.
Training & Skill Development
As the shift to digital continues, there’ll be a need for specialized skills to manage, navigate, and interpret these advanced systems. Institutions should focus on training their manpower to adapt to the demands of this data-driven era.
Regulations, Standards, and Protocols
- Regulations: Ethical concerns will arise with digital transformation. Hence, governments and international bodies must fast-track establishment of laws governing data collection, storage, access, and privacy.
- Standards: The scientific community should set global standards to ensure consistency and reliability of research data. This will further enable interoperability of databases worldwide.
- Protocols: In the event of future pandemics, having a set of universally recognized and rapidly implementable protocols can assist in quick data collection and analysis, therefore making the response more effective.
The quest for improved methods for collecting and searching pandemic studies is perhaps one of the most critical undertakings today. Given the potential long-term implications and significant outcomes, concerted efforts are necessary to leverage advanced technology, upskill the workforce, and establish the needed regulations, standards, and protocols.
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by jsendak | Mar 22, 2024 | GR & QC Articles
arXiv:2403.13824v1 Announce Type: new
Abstract: This letter presents a novel model that characterizes the curvature of space-time, influenced by a massive gauge field in the early universe. This curvature can lead to a multitude of observations, including the Hubble tension issue and the isotropic stochastic gravitational-wave background. We introduce, for the first time, the concept of gauge field Hopfions, which exist in the space-time. We further investigate how hopfions can influence Hubble parameter values. Our findings open the door to utilizing hopfions as a topological source which links both gravitation and the gauge field.
Curvature of Space-Time and Hubble Tension: A Novel Model
This letter presents a groundbreaking model that offers new insights into the curvature of space-time in the early universe. Our research demonstrates that this curvature is influenced by a massive gauge field, which opens up a world of possibilities for understanding various astronomical phenomena.
One prominent issue in cosmology is the Hubble tension, which refers to the discrepancy between the measured and predicted values of the Hubble constant. Our model provides a potential explanation for this tension by incorporating the influence of the gauge field on space-time curvature. By taking into account the presence of gauge field Hopfions, which are topological objects in space-time, we find that they play a significant role in determining the Hubble parameter values.
Unleashing the Power of Hopfions
The concept of gauge field Hopfions, introduced for the first time in our research, holds immense potential for revolutionizing our understanding of the interplay between gravitation and the gauge field. These topological objects can be viewed as a unique source that contributes to the overall curvature of space-time.
By investigating the influence of hopfions on the Hubble parameter, we not only shed light on the Hubble tension issue but also provide a novel avenue for studying the behavior of gravitational waves. The presence of hopfions leads to the emergence of an isotropic stochastic gravitational-wave background, which can have far-reaching implications for gravitational wave detection and analysis.
A Future Roadmap for Readers
As we move forward, there are several challenges and opportunities that lie ahead in further exploring and harnessing the potential of our novel model:
- Experimental Verification: One key challenge is to devise experiments or observational techniques that can provide empirical evidence supporting our model. This would involve detecting the presence of gauge field Hopfions or finding indirect observations of the isotropic gravitational-wave background.
- Refinement and Validation: It is essential to refine and validate our model through rigorous theoretical calculations and simulations. This would help strengthen the theoretical foundations and ensure the consistency and accuracy of our conclusions.
- Broader Implications: Exploring the broader implications of the interplay between gauge field Hopfions, gravitation, and the gauge field is an exciting avenue for future research. This could potentially lead to advancements in fields such as quantum gravity and high-energy physics.
- Technological Applications: Understanding the behavior of gauge field Hopfions and their impact on space-time curvature could pave the way for new technological applications. This may include the development of novel gravitational wave detectors or finding applications in quantum information processing and communication.
In conclusion, our research offers a fresh perspective on the curvature of space-time and its connection to the gauge field. By introducing the concept of gauge field Hopfions, we have provided a potential explanation for the Hubble tension issue and opened up new avenues for exploring the behavior of gravitational waves. While challenges and opportunities lie ahead, this model has the potential to reshape our understanding of the fundamental forces that govern the universe.
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by jsendak | Mar 14, 2024 | Computer Science
arXiv:2403.07938v1 Announce Type: cross
Abstract: In recent times, the focus on text-to-audio (TTA) generation has intensified, as researchers strive to synthesize audio from textual descriptions. However, most existing methods, though leveraging latent diffusion models to learn the correlation between audio and text embeddings, fall short when it comes to maintaining a seamless synchronization between the produced audio and its video. This often results in discernible audio-visual mismatches. To bridge this gap, we introduce a groundbreaking benchmark for Text-to-Audio generation that aligns with Videos, named T2AV-Bench. This benchmark distinguishes itself with three novel metrics dedicated to evaluating visual alignment and temporal consistency. To complement this, we also present a simple yet effective video-aligned TTA generation model, namely T2AV. Moving beyond traditional methods, T2AV refines the latent diffusion approach by integrating visual-aligned text embeddings as its conditional foundation. It employs a temporal multi-head attention transformer to extract and understand temporal nuances from video data, a feat amplified by our Audio-Visual ControlNet that adeptly merges temporal visual representations with text embeddings. Further enhancing this integration, we weave in a contrastive learning objective, designed to ensure that the visual-aligned text embeddings resonate closely with the audio features. Extensive evaluations on the AudioCaps and T2AV-Bench demonstrate that our T2AV sets a new standard for video-aligned TTA generation in ensuring visual alignment and temporal consistency.
Bridging the Gap between Text-to-Audio Generation and Video Alignment
In the field of multimedia information systems, text-to-audio (TTA) generation has gained increasing attention. Researchers are continuously striving to synthesize high-quality audio content from textual descriptions. However, one major challenge faced by existing methods is the lack of seamless synchronization between the generated audio and its corresponding video, resulting in noticeable audio-visual mismatches. To address this issue, a groundbreaking benchmark called T2AV-Bench has been introduced to evaluate the visual alignment and temporal consistency of TTA generation models aligned with videos.
The T2AV-Bench benchmark is designed to bridge the gap by offering three novel metrics dedicated to assessing visual alignment and temporal consistency. These metrics serve as a robust evaluation framework for TTA generation models. By leveraging these metrics, researchers can better understand and improve the performance of their models in terms of audio-visual synchronization.
In addition to the benchmark, a new TTA generation model called T2AV has been presented. T2AV goes beyond traditional methods by incorporating visual-aligned text embeddings into its latent diffusion approach. This integration allows T2AV to effectively capture temporal nuances from video data, ensuring a more accurate and natural alignment between the generated audio and the video content. This is achieved through the utilization of a temporal multi-head attention transformer, which extracts and understands temporal information from the video data.
T2AV also introduces an innovative component called the Audio-Visual ControlNet, which merges temporal visual representations with text embeddings. This integration enhances the overall alignment and coherence between the audio and video components. To further improve the synchronization, a contrastive learning objective is employed to ensure that the visual-aligned text embeddings closely resonate with the audio features.
The evaluations conducted on the AudioCaps and T2AV-Bench datasets demonstrate the effectiveness of the T2AV model. It sets a new standard for video-aligned TTA generation by significantly improving visual alignment and temporal consistency. These advancements have direct implications for various applications in the field of multimedia systems, such as animations, artificial reality (AR), augmented reality (AR), and virtual reality (VR).
The multi-disciplinary nature of the concepts presented in this content showcases the intersection between natural language processing, computer vision, and audio processing. The integration of these disciplines is crucial for developing more advanced and realistic TTA generation models that can seamlessly align audio and video content. By addressing the shortcomings of existing methods and introducing innovative techniques, this research paves the way for future advancements in multimedia information systems.
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by jsendak | Mar 11, 2024 | AI
arXiv:2403.05029v1 Announce Type: new Abstract: Traffic prediction is one of the most significant foundations in Intelligent Transportation Systems (ITS). Traditional traffic prediction methods rely only on historical traffic data to predict traffic trends and face two main challenges. 1) insensitivity to unusual events. 2) limited performance in long-term prediction. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation, and name the task Text-to-Traffic Generation (TTG). The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations. To this end, we propose ChatTraffic, the first diffusion model for text-to-traffic generation. To guarantee the consistency between synthetic and real data, we augment a diffusion model with the Graph Convolutional Network (GCN) to extract spatial correlations of traffic data. In addition, we construct a large dataset containing text-traffic pairs for the TTG task. We benchmarked our model qualitatively and quantitatively on the released dataset. The experimental results indicate that ChatTraffic can generate realistic traffic situations from the text. Our code and dataset are available at https://github.com/ChyaZhang/ChatTraffic.
The article “Text-to-Traffic Generation: A Diffusion Model Approach” addresses the challenges faced by traditional traffic prediction methods and introduces a novel approach called Text-to-Traffic Generation (TTG). The TTG task aims to generate traffic situations by combining generative models with text descriptions of the traffic system. The key challenge lies in associating text with the spatial structure of the road network and traffic data. The authors propose ChatTraffic, the first diffusion model for text-to-traffic generation, which incorporates a Graph Convolutional Network (GCN) to extract spatial correlations. They also construct a large dataset of text-traffic pairs for benchmarking purposes. The experimental results demonstrate that ChatTraffic can generate realistic traffic situations from text descriptions. The code and dataset for this model are publicly available.
Traffic Prediction and Text-to-Traffic Generation: Paving the Way for Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) have become an integral part of modern urban infrastructure, aiming to enhance traffic management and efficiency. One of the foundational pillars of ITS is traffic prediction, which enables authorities to anticipate traffic trends and plan proactive measures to alleviate congestion. However, traditional traffic prediction methods have their limitations, mainly due to their reliance solely on historical traffic data. This article explores a novel approach to traffic prediction by combining generative models with text descriptions of the traffic system, introducing the concept of Text-to-Traffic Generation (TTG).
The Challenges of Traditional Traffic Prediction
Traditional traffic prediction methods face two significant challenges. Firstly, they tend to be insensitive to unusual events such as accidents or major construction, which can lead to unpredictable traffic patterns. Secondly, these methods often display limited performance in long-term prediction, struggling to capture complex and evolving traffic dynamics. Addressing these challenges is crucial to developing more accurate and reliable traffic prediction models.
The Emergence of Text-to-Traffic Generation
Text-to-Traffic Generation (TTG) offers a fresh perspective on traffic prediction by incorporating textual information along with historical traffic data. The key challenge of the TTG task lies in effectively associating text descriptions with the spatial structure of the road network and traffic data to generate realistic traffic situations. In response to this, researchers have introduced ChatTraffic, the first diffusion model designed specifically for text-to-traffic generation.
The Role of ChatTraffic in Traffic Generation
ChatTraffic utilizes a diffusion model augmented with Graph Convolutional Network (GCN) to extract spatial correlations from traffic data. By incorporating text descriptions, ChatTraffic ensures consistency between synthetic and real data, improving the reliability of traffic generation. The model leverages a large dataset containing text-traffic pairs specifically constructed for the TTG task.
Benchmarking ChatTraffic: Qualitative and Quantitative Evaluation
To evaluate the performance of ChatTraffic, the model has been benchmarked both qualitatively and quantitatively using the released dataset. The experimental results demonstrate that ChatTraffic is capable of generating realistic traffic situations based on textual inputs. This breakthrough in traffic generation opens up new possibilities for forecasting traffic patterns with greater accuracy and capturing the effects of unusual events on traffic dynamics.
The Road Ahead
The introduction of Text-to-Traffic Generation (TTG) through models like ChatTraffic showcases the potential of leveraging textual context to enhance traffic prediction. As research advances in this field, further improvements and innovations can be expected, leading to more efficient traffic management and intelligent transportation systems. The availability of the ChatTraffic code and dataset on GitHub (https://github.com/ChyaZhang/ChatTraffic) enables the wider research community to explore and contribute to this exciting development.
The paper introduces a novel approach called ChatTraffic, which combines generative models with text descriptions of the traffic system to generate realistic traffic situations. This task, referred to as Text-to-Traffic Generation (TTG), aims to address the limitations of traditional traffic prediction methods that rely solely on historical traffic data.
One of the key challenges in the TTG task is how to associate the textual information with the spatial structure of the road network and traffic data. To overcome this challenge, the authors propose augmenting a diffusion model with a Graph Convolutional Network (GCN) to extract spatial correlations from the traffic data. This allows the generated traffic situations to be consistent with real data.
To evaluate the performance of ChatTraffic, the authors construct a large dataset containing text-traffic pairs specifically for the TTG task. They then benchmark their model both qualitatively and quantitatively using this dataset. The experimental results demonstrate that ChatTraffic is capable of generating realistic traffic situations from the provided text descriptions.
This research has significant implications for Intelligent Transportation Systems (ITS) as it offers a new approach to traffic prediction that overcomes the challenges of insensitivity to unusual events and limited long-term prediction performance. By incorporating text descriptions, ChatTraffic has the potential to improve the accuracy and reliability of traffic prediction models.
Moving forward, it would be interesting to see further advancements in this field. For instance, exploring the use of more advanced generative models, such as Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs), could potentially enhance the realism of the generated traffic situations. Additionally, incorporating real-time data sources, such as social media feeds or weather information, could further improve the predictive capabilities of ChatTraffic by capturing dynamic factors that influence traffic patterns.
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