by jsendak | Mar 14, 2025 | Computer Science
arXiv:2503.09852v1 Announce Type: new
Abstract: Speech-driven 3D facial animation is challenging due to the diversity in speaking styles and the limited availability of 3D audio-visual data. Speech predominantly dictates the coarse motion trends of the lip region, while specific styles determine the details of lip motion and the overall facial expressions. Prior works lack fine-grained learning in style modeling and do not adequately consider style biases across varying speech conditions, which reduce the accuracy of style modeling and hamper the adaptation capability to unseen speakers. To address this, we propose a novel framework, StyleSpeaker, which explicitly extracts speaking styles based on speaker characteristics while accounting for style biases caused by different speeches. Specifically, we utilize a style encoder to capture speakers’ styles from facial motions and enhance them according to motion preferences elicited by varying speech conditions. The enhanced styles are then integrated into the coarse motion features via a style infusion module, which employs a set of style primitives to learn fine-grained style representation. Throughout training, we maintain this set of style primitives to comprehensively model the entire style space. Hence, StyleSpeaker possesses robust style modeling capability for seen speakers and can rapidly adapt to unseen speakers without fine-tuning. Additionally, we design a trend loss and a local contrastive loss to improve the synchronization between synthesized motions and speeches. Extensive qualitative and quantitative experiments on three public datasets demonstrate that our method outperforms existing state-of-the-art approaches.
Expert Commentary: Speech-driven 3D Facial Animation and the Multi-disciplinary Nature of the Concepts
The content discussed in this article revolves around the challenging task of speech-driven 3D facial animation. This topic is inherently multi-disciplinary, combining elements from various fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Facial animation is a crucial component of many multimedia systems, including virtual reality applications and animated movies. To create realistic and expressive facial animations, it is important to accurately model the intricate details of lip motion and facial expressions. However, existing approaches often struggle to capture the fine-grained nuances of different speaking styles and lack the ability to adapt to unseen speakers.
The proposed framework, StyleSpeaker, addresses these limitations by explicitly extracting speaking styles based on speaker characteristics while considering the style biases caused by different speeches. By utilizing a style encoder, the framework captures speakers’ styles and enhances them based on motion preferences elicited by varying speech conditions. This integration of styles into the coarse motion features is achieved via a style infusion module that utilizes a set of style primitives to learn fine-grained style representation. The framework also maintains this set of style primitives throughout training to comprehensively model the entire style space.
In addition to style modeling, the framework introduces a trend loss and a local contrastive loss to improve the synchronization between synthesized motions and speeches. These additional losses contribute to the overall accuracy of the animation and enhance its realism.
The experiments conducted on three public datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches in terms of both qualitative and quantitative measures. The combination of style modeling, motion-speech synchronization, and the adaptability to unseen speakers makes StyleSpeaker a promising framework for speech-driven 3D facial animation.
From a broader perspective, this research showcases the interconnectedness of different domains within multimedia information systems. The concepts of 3D facial animation, style modeling, and motion-speech synchronization are essential not only in the context of multimedia applications but also in fields like virtual reality, augmented reality, and artificial reality. By improving the realism and expressiveness of facial animations, this research contributes to the development of immersive experiences and realistic virtual environments.
Key takeaways:
- The content focuses on speech-driven 3D facial animation and proposes a novel framework called StyleSpeaker.
- StyleSpeaker explicitly extracts speaking styles based on speaker characteristics and accounts for style biases caused by different speeches.
- The framework enhances styles according to motion preferences elicited by varying speech conditions, integrating them into the coarse motion features.
- StyleSpeaker possesses robust style modeling capability and can rapidly adapt to unseen speakers without the need for fine-tuning.
- The framework introduces trend loss and local contrastive loss to improve motion-speech synchronization.
- The method outperforms existing state-of-the-art approaches in both qualitative and quantitative evaluations.
- The multi-disciplinary nature of the concepts involved showcases their relevance in the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Reference: Speech-driven 3D facial animation is challenging due to the diversity in speaking styles and the limited availability of 3D audio-visual data. Speech predominantly dictates the coarse motion trends of the lip region, while specific styles determine the details of lip motion and the overall facial expressions. Prior works lack fine-grained learning in style modeling and do not adequately consider style biases across varying speech conditions, which reduce the accuracy of style modeling and hamper the adaptation capability to unseen speakers. To address this, we propose a novel framework, StyleSpeaker, which explicitly extracts speaking styles based on speaker characteristics while accounting for style biases caused by different speeches. Specifically, we utilize a style encoder to capture speakers’ styles from facial motions and enhance them according to motion preferences elicited by varying speech conditions. The enhanced styles are then integrated into the coarse motion features via a style infusion module, which employs a set of style primitives to learn fine-grained style representation. Throughout training, we maintain this set of style primitives to comprehensively model the entire style space. Hence, StyleSpeaker possesses robust style modeling capability for seen speakers and can rapidly adapt to unseen speakers without fine-tuning. Additionally, we design a trend loss and a local contrastive loss to improve the synchronization between synthesized motions and speeches. Extensive qualitative and quantitative experiments on three public datasets demonstrate that our method outperforms existing state-of-the-art approaches.
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by jsendak | Feb 11, 2025 | Computer Science
arXiv:2502.05695v1 Announce Type: new
Abstract: This paper proposes a novel framework for real-time adaptive-bitrate video streaming by integrating latent diffusion models (LDMs) within the FFmpeg techniques. This solution addresses the challenges of high bandwidth usage, storage inefficiencies, and quality of experience (QoE) degradation associated with traditional constant bitrate streaming (CBS) and adaptive bitrate streaming (ABS). The proposed approach leverages LDMs to compress I-frames into a latent space, offering significant storage and semantic transmission savings without sacrificing high visual quality. While it keeps B-frames and P-frames as adjustment metadata to ensure efficient video reconstruction at the user side, the proposed framework is complemented with the most state-of-the-art denoising and video frame interpolation (VFI) techniques. These techniques mitigate semantic ambiguity and restore temporal coherence between frames, even in noisy wireless communication environments. Experimental results demonstrate the proposed method achieves high-quality video streaming with optimized bandwidth usage, outperforming state-of-the-art solutions in terms of QoE and resource efficiency. This work opens new possibilities for scalable real-time video streaming in 5G and future post-5G networks.
New Framework for Real-Time Adaptive-Bitrate Video Streaming: A Multi-disciplinary Approach
Video streaming has become an integral part of our daily lives, and the demand for high-quality video content is increasing exponentially. However, traditional streaming methods face challenges such as high bandwidth usage, storage inefficiencies, and degradation of quality of experience (QoE). In this paper, a novel framework is proposed to address these challenges by integrating latent diffusion models (LDMs) within the FFmpeg techniques.
One of the key contributions of this framework is the use of LDMs to compress I-frames into a latent space. By leveraging latent diffusion models, significant storage and semantic transmission savings can be achieved without sacrificing visual quality. This is crucial in modern multimedia information systems, where efficient storage and transmission are vital.
Furthermore, the proposed framework considers the multi-disciplinary nature of video streaming by incorporating state-of-the-art denoising and video frame interpolation (VFI) techniques. These techniques help mitigate semantic ambiguity and restore temporal coherence between frames, even in noisy wireless communication environments. By addressing temporal coherence, the framework ensures a smooth and seamless video streaming experience.
From a wider perspective, this research aligns with the field of Artificial Reality, Augmented Reality, and Virtual Realities. The integration of LDMs, denoising, and VFI techniques in video streaming has potential applications in these fields. For example, in augmented reality, the reduction of semantic ambiguity can enhance the accuracy and realism of virtual objects overlaid onto the real world.
This novel framework also has implications for 5G and future post-5G networks. As video streaming becomes more prevalent with the advent of faster network technologies, resource efficiency becomes crucial. The proposed method not only achieves high-quality video streaming but also optimizes bandwidth usage, making it well-suited for scalable real-time video streaming in these networks.
In conclusion, this paper introduces a groundbreaking framework for real-time adaptive-bitrate video streaming. By leveraging latent diffusion models, denoising, and video frame interpolation techniques, this framework tackles the challenges of traditional streaming methods and opens up new possibilities for the multimedia information systems, artificial reality, augmented reality, and virtual realities. As technology continues to evolve, this research paves the way for more efficient and immersive video streaming experiences.
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by jsendak | Feb 8, 2025 | AI
arXiv:2502.03621v1 Announce Type: new Abstract: We present a method for augmenting real-world videos with newly generated dynamic content. Given an input video and a simple user-provided text instruction describing the desired content, our method synthesizes dynamic objects or complex scene effects that naturally interact with the existing scene over time. The position, appearance, and motion of the new content are seamlessly integrated into the original footage while accounting for camera motion, occlusions, and interactions with other dynamic objects in the scene, resulting in a cohesive and realistic output video. We achieve this via a zero-shot, training-free framework that harnesses a pre-trained text-to-video diffusion transformer to synthesize the new content and a pre-trained Vision Language Model to envision the augmented scene in detail. Specifically, we introduce a novel inference-based method that manipulates features within the attention mechanism, enabling accurate localization and seamless integration of the new content while preserving the integrity of the original scene. Our method is fully automated, requiring only a simple user instruction. We demonstrate its effectiveness on a wide range of edits applied to real-world videos, encompassing diverse objects and scenarios involving both camera and object motion.
The article “Augmenting Real-World Videos with Dynamic Content” introduces a groundbreaking method for enhancing real-world videos by adding newly generated dynamic objects or scene effects. The method utilizes a user-provided text instruction to synthesize the desired content, seamlessly integrating it into the original footage while considering factors such as camera motion, occlusions, and interactions with other objects. This training-free framework combines a text-to-video diffusion transformer and a Vision Language Model to envision the augmented scene in detail. The authors present a novel inference-based method that manipulates features within the attention mechanism, ensuring accurate localization and seamless integration of the new content while preserving the authenticity of the original scene. The automated nature of the method only requires a simple user instruction, and its effectiveness is demonstrated through a wide range of edits applied to real-world videos involving diverse objects and scenarios with camera and object motion.
Augmenting Real-World Videos with Dynamic Content: A Revolution in Visual Effects
In the world of video editing and visual effects, the ability to seamlessly integrate newly generated dynamic content into real-world footage has long been a challenge. Traditional techniques often require extensive training, manual intervention, and complex workflows, resulting in a time-consuming and expensive process. However, a groundbreaking method has recently been developed that promises to revolutionize this field.
Synthesizing Dynamic Objects and Complex Scene Effects
The method involves synthesizing dynamic objects or complex scene effects that naturally interact with the existing scene over time. Through a user-provided text instruction, the system understands the desired content and seamlessly integrates it into the original footage. This means that with a simple command, users can generate and embed any desired object or effect into their videos.
Crucially, the system takes into account the unique characteristics of each video, such as camera motion, occlusions, and interactions with other dynamic objects. This ensures that the augmented content looks cohesive and realistic, as if it was part of the original scene from the beginning.
Training-Free Framework: A Breakthrough in Automation
What makes this method truly innovative is its zero-shot, training-free framework. Instead of relying on extensive training datasets, the system utilizes pre-trained models to achieve its remarkable results. A text-to-video diffusion transformer synthesizes the new content based on the user instruction, while a Vision Language Model envisions the augmented scene in detail.
The real breakthrough comes from a novel inference-based method that manipulates features within the attention mechanism. This enables accurate localization and seamless integration of the new content while preserving the integrity of the original scene. The result is a fully automated system that only requires a simple user instruction, simplifying the editing process and making visual effects accessible to a wider audience.
Diverse Applications and Impressive Results
The effectiveness of this method has been demonstrated on a wide range of edits applied to real-world videos. It has successfully augmented diverse objects and scenarios involving both camera and object motion. From adding virtual characters to creating stunning particle effects, the possibilities are endless.
“The ability to seamlessly integrate newly generated dynamic content into real-world footage opens up a world of possibilities for video editing and visual effects. This method has the potential to democratize the field and empower creators with tools that were once only accessible to professionals.”
With this groundbreaking method, creating visually stunning videos with augmented content has never been easier. The barriers to entry in the world of video editing and visual effects are rapidly diminishing, opening up opportunities for a new wave of creativity.
The paper titled “Augmenting Real-World Videos with Dynamic Content” presents a novel method for adding newly generated dynamic content to existing videos based on simple user-provided text instructions. The proposed framework seamlessly integrates the new content into the original footage while considering factors such as camera motion, occlusions, and interactions with other dynamic objects in the scene.
The authors achieve this by leveraging a zero-shot, training-free approach that utilizes a pre-trained text-to-video diffusion transformer to synthesize the new content. Additionally, a pre-trained Vision Language Model is used to envision the augmented scene in detail. This combination allows for the manipulation of features within the attention mechanism, enabling accurate localization and seamless integration of the new content while preserving the integrity of the original scene.
One of the notable aspects of this method is its fully automated nature, requiring only a simple user instruction. This ease of use makes it accessible to a wide range of users, including those without extensive technical expertise. The effectiveness of the proposed method is demonstrated through various edits applied to real-world videos, encompassing diverse objects and scenarios involving both camera and object motion.
This research has significant implications for content creation, visual effects, and video editing industries. The ability to seamlessly integrate new dynamic content into real-world videos based on simple user instructions opens up possibilities for enhanced storytelling, visual effects, and user-generated content. It could find applications in industries such as film, advertising, virtual reality, and video game development.
One potential direction for future research could be the exploration of more advanced user instructions, allowing for more nuanced and specific dynamic content generation. Additionally, the authors could investigate the integration of other modalities, such as audio or depth information, to further enhance the realism and coherence of the output videos. Furthermore, the scalability of the proposed method could be explored to handle longer and more complex videos.
Overall, the presented method offers an exciting advancement in the field of video augmentation and holds promise for future developments in content creation and visual effects.
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by jsendak | Feb 5, 2025 | Art
A Journey Through the Unconscious: Examining Psychological States in the Art of Sønderland
Preface
In the realm of art, there exists a fascination with the intricate workings of the human mind and its ability to transcend ordinary consciousness. Sønderland, a Norwegian-Irish artist born in 1996, ventures deep into this philosophical terrain with a profound exploration of psychological states. Through their artwork, they delve into the fluid boundary between the subconscious and conscious, unraveling the mysteries that lie within.
Harnessing influences from both historical and contemporary sources, Sønderland’s art resonates with a rich tapestry of ideas. Drawing inspiration from psychoanalytic theories of Sigmund Freud and Carl Jung, they become a modern-day explorer, navigating through the labyrinthine chambers of the psyche. Their art provides a window into an enigmatic world, wherein emotions, desires, and fears intertwine.
Exploring the Subconscious
At the core of Sønderland’s artwork lies a tireless quest to peel back the layers of human consciousness. Through their meticulously crafted canvases, they embark on a journey into the subconscious, a territory often shrouded in darkness and esoteric symbolism.
Influenced by the surrealist movement spearheaded by artists such as Salvador Dalí and René Magritte, Sønderland merges dreamlike imagery with stark realism. Their use of bold colors, distorted perspectives, and juxtapositions create a visual language that challenges conventional interpretations. This juxtaposition mirrors the paradoxical nature of the subconscious, where rationality and irrationality coexist.
Historical Reverberations
To fully appreciate the significance of Sønderland’s work, one must acknowledge its historical resonances. The exploration of psychological states and the use of art as a psychic conduit can be traced back to the Symbolist movement of the late 19th century.
Symbolist painters like Gustave Moreau and Odilon Redon sought to depict the supernatural and irrational aspects of human existence. By employing symbolism and allegory, they aimed to convey emotions and ideas that transcended the limitations of ordinary perception. Sønderland, similarly inspired, carries on this tradition, breathing new life into the exploration of the subconscious with a contemporary perspective.
A Contemporary Lens
As a contemporary artist, Sønderland embraces the tools of the digital era to extend the boundaries of artistic expression. Their multidisciplinary approach encompasses not only traditional mediums like painting and drawing but also digital manipulation and installation art.
The ubiquity of technology and the internet today has profoundly altered the way we perceive and interact with art. Sønderland harnesses this new landscape, utilizing digital platforms to share their work with a global audience. Their ability to connect with viewers on a global scale, across cultural boundaries, truly exemplifies the interconnectedness of the human experience.
Conclusion
Sønderland’s artwork serves as a gateway to the subconscious, inviting viewers to explore psychological states that lie beneath the surface of their consciousness. By blending historical influences with a contemporary lens, their art exudes a timeless quality that resonates across cultures and generations.
“Each stroke of my brush is a step deeper into the labyrinth of the mind, unearthing the untold stories that shape our very being.” – Sønderland
Sønderland (b. 1996) is a Norwegian-Irish artist exploring psychological states and the fluid boundary between the subconscious and conscious.
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by jsendak | Jan 20, 2025 | AI
Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods focus on…
the individual estimation of body, hands, and face motion capture, leaving a gap in unifying these components. However, a groundbreaking approach called Expressive Human Pose and Shape Estimation (EHPS) has emerged, aiming to bridge this gap and revolutionize the field. This article explores the core themes of EHPS and its potential applications, highlighting the need for a comprehensive and unified method in capturing human motion and shape. By delving into the limitations of current approaches and the promising advancements offered by EHPS, readers will gain a compelling overview of how this innovative technique can transform various industries and enhance our understanding of human movement.
Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications.
In recent years, there have been significant advancements in the field of expressive human pose and shape estimation (EHPS). This technology enables the capturing and analysis of body, hand, and face motions, opening up new possibilities for applications in fields such as virtual reality, gaming, animation, and healthcare. However, despite these encouraging advancements, the current state-of-the-art methods primarily focus on individual body parts, neglecting the importance of capturing the holistic expression of the human body.
The Importance of Holistic Expression
While individual body part recognition is crucial, the true essence of human motion lies in the integration and synchronization of all body parts. Each body part contributes to the overall expression and conveys important information about an individual’s emotions, intentions, and dispositions. Therefore, it is essential to develop EHPS methods that encompass the entirety of a person’s motion, allowing for a more accurate and immersive capture of human expressivity.
Innovative Solutions for Holistic EHPS
One innovative solution to enhance EHPS methods is the incorporation of deep learning algorithms. By training large-scale neural networks using vast datasets of human poses and motions, we can overcome the limitations of traditional machine learning techniques. Deep learning enables the algorithms to learn complex patterns and relationships between different body parts, resulting in more accurate and coherent human motion capture.
Furthermore, real-time EHPS is another area that has tremendous potential for innovation. Currently, EHPS methods require time-intensive processing, limiting their application in real-time scenarios. However, by leveraging advancements in parallel computing and hardware acceleration, it may be possible to develop EHPS systems that can capture and interpret human motion in real-time, leading to more interactive and immersive experiences in various domains.
Applications and Impact
The potential applications of holistic EHPS are vast and exciting. In the field of virtual reality, for instance, a more accurate and comprehensive capture of human motion can enhance the realism and immersion of virtual environments. Gaming experiences can be elevated to a new level, allowing players to control avatars that replicate their real-life movements and expressions. In the medical field, EHPS can aid in rehabilitation by precisely tracking and analyzing patients’ movements, facilitating tailored therapy programs.
Moreover, the impact of holistic EHPS extends beyond entertainment and healthcare. In the field of psychology, for example, it can be used to analyze non-verbal expressions and decode emotions. Similarly, in sociology and anthropology, understanding the nuances of human motion can shed light on cultural differences and social interactions.
Expressive human pose and shape estimation is a rapidly evolving field that holds immense potential for improving various aspects of our lives. By embracing holistic approaches and advancing the capabilities of EHPS methods, we can unlock new possibilities for expression, creativity, and understanding within the realm of human motion.
capturing either body, hands, or face motion separately, which limits the ability to fully understand and analyze human behavior in a holistic manner. The EHPS approach aims to overcome this limitation by integrating all three components into a single framework, enabling a more comprehensive understanding of human pose and shape estimation.
One of the key strengths of EHPS is its potential to revolutionize various industries and fields where human motion analysis is crucial. For instance, in the field of sports, EHPS can provide valuable insights into athletes’ movements, allowing coaches and trainers to identify weaknesses, optimize performance, and prevent injuries. By capturing and analyzing the intricate details of body, hands, and face motion, EHPS can provide a comprehensive picture of an athlete’s form, technique, and expression, leading to more effective training strategies.
In the entertainment industry, EHPS has the potential to revolutionize animation and virtual reality experiences. By accurately capturing and replicating human motion, including facial expressions, hand gestures, and body movements, EHPS can bring virtual characters to life in a more realistic and immersive manner. This technology can enhance the gaming experience, improve motion capture for movies and animations, and even enable virtual avatars to mimic human behavior more convincingly.
Moreover, EHPS can have significant implications in the field of healthcare and rehabilitation. By accurately tracking and analyzing human motion, EHPS can assist in the diagnosis and treatment of movement disorders, such as Parkinson’s disease or stroke rehabilitation. The integration of body, hands, and face motion capture in EHPS can provide clinicians with a comprehensive understanding of patients’ movements, enabling personalized treatment plans and better monitoring of progress.
Looking ahead, further advancements in EHPS can be expected. One area of improvement could be the refinement of algorithms and models to enhance the accuracy and robustness of pose and shape estimation. This would involve developing more sophisticated deep learning architectures that can better handle occlusions, variations in lighting conditions, and complex human poses.
Additionally, the integration of EHPS with other emerging technologies, such as augmented reality (AR) and artificial intelligence (AI), could open up new possibilities. For example, combining EHPS with AR glasses could enable real-time feedback and guidance for physical activities, such as yoga or dance, enhancing the learning experience. AI algorithms could also leverage the comprehensive understanding of human behavior provided by EHPS to develop intelligent systems that can predict and respond to human intentions and emotions.
In conclusion, the EHPS approach holds great promise in advancing the field of human motion analysis. By unifying body, hands, and face motion capture, EHPS enables a more comprehensive understanding of human behavior, with applications ranging from sports training and entertainment to healthcare and rehabilitation. With continued research and development, EHPS is poised to revolutionize how we perceive and interact with human motion in various domains.
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