by jsendak | Apr 9, 2025 | AI
arXiv:2504.05686v1 Announce Type: cross Abstract: Robustness is critical in zero-shot singing voice conversion (SVC). This paper introduces two novel methods to strengthen the robustness of the kNN-VC framework for SVC. First, kNN-VC’s core representation, WavLM, lacks harmonic emphasis, resulting in dull sounds and ringing artifacts. To address this, we leverage the bijection between WavLM, pitch contours, and spectrograms to perform additive synthesis, integrating the resulting waveform into the model to mitigate these issues. Second, kNN-VC overlooks concatenative smoothness, a key perceptual factor in SVC. To enhance smoothness, we propose a new distance metric that filters out unsuitable kNN candidates and optimize the summing weights of the candidates during inference. Although our techniques are built on the kNN-VC framework for implementation convenience, they are broadly applicable to general concatenative neural synthesis models. Experimental results validate the effectiveness of these modifications in achieving robust SVC. Demo: http://knnsvc.com Code: https://github.com/SmoothKen/knn-svc
The article “Robustness Enhancement in Zero-Shot Singing Voice Conversion” introduces two innovative methods to improve the robustness of the kNN-VC framework for singing voice conversion (SVC). The kNN-VC framework’s core representation, WavLM, lacks harmonic emphasis, resulting in dull sounds and ringing artifacts. To address this issue, the authors leverage the relationship between WavLM, pitch contours, and spectrograms to perform additive synthesis, integrating the resulting waveform into the model to mitigate these problems. Furthermore, the kNN-VC framework overlooks concatenative smoothness, a crucial perceptual factor in SVC. To enhance smoothness, the authors propose a new distance metric that filters out inappropriate kNN candidates and optimizes the summing weights of the candidates during inference. Although these techniques are specifically designed for the kNN-VC framework, they can be broadly applied to general concatenative neural synthesis models. The effectiveness of these modifications is validated through experimental results, demonstrating their ability to achieve robust SVC. Readers can access a demo of the enhanced framework at http://knnsvc.com and find the code for implementation on GitHub at https://github.com/SmoothKen/knn-svc.
Enhancing Robustness in Zero-Shot Singing Voice Conversion
Zero-shot singing voice conversion (SVC) has gained significant attention in recent years due to its potential applications in the music industry. However, achieving robustness in SVC remains a critical challenge. In this article, we explore the underlying themes and concepts of the kNN-VC framework for SVC and propose two novel methods to strengthen its robustness.
1. Addressing Dull Sounds and Ringing Artifacts
The core representation of the kNN-VC framework, known as WavLM, has been found lacking in harmonic emphasis, resulting in dull sounds and ringing artifacts. To overcome this limitation, we leverage the bijection between WavLM, pitch contours, and spectrograms to perform additive synthesis.
By integrating the resulting waveform into the model, we can mitigate the dull sounds and ringing artifacts, resulting in a more natural and pleasant vocal output. This enhancement not only improves the overall quality of the converted voice but also adds a new layer of realism to the synthesized vocal performance.
2. Enhancing Concatenative Smoothness in SVC
Another important aspect of vocal conversion is the perception of smoothness, which is often overlooked in the kNN-VC framework. Concatenative smoothness refers to the seamless transition between different segments of the converted voice, ensuring a coherent and natural flow.
To enhance smoothness, we propose a new distance metric that filters out unsuitable kNN candidates during the inference process. This filtering mechanism helps eliminate potential discontinuities and inconsistencies, contributing to a more coherent and smooth output. Additionally, we optimize the summing weights of the selected candidates, further refining the smoothness of the converted voice.
Broad Applicability to Concatenative Neural Synthesis Models
While our techniques are specifically built on the kNN-VC framework for implementation convenience, they have broader applicability to general concatenative neural synthesis models. The principles behind additive synthesis and the emphasis on smoothness can be applied to other frameworks and models to achieve robustness in various singing voice conversion tasks.
Experimental results have validated the effectiveness of these modifications in achieving robust SVC. The proposed methods have significantly improved the quality, realism, and smoothness of the converted voice, enhancing the overall user experience in zero-shot singing voice conversion applications.
To experience a live demonstration of the enhanced SVC, you can visit the demo website. For more technical details, the implementation code can be found on GitHub.
Enhancing robustness in zero-shot singing voice conversion opens up new possibilities in the music industry. These advancements pave the way for more immersive and realistic vocal synthesis applications, revolutionizing the way we create and enjoy music.
The paper titled “Robustness Enhancement in Zero-shot Singing Voice Conversion” introduces two innovative methods to improve the robustness of the kNN-VC (k-Nearest Neighbors Voice Conversion) framework for singing voice conversion (SVC). This research is crucial as robustness is a critical factor in SVC systems.
The first method addresses the issue of the core representation of kNN-VC, called WavLM, lacking harmonic emphasis and resulting in dull sounds and ringing artifacts. To overcome this limitation, the authors propose leveraging the relationship between WavLM, pitch contours, and spectrograms to perform additive synthesis. By integrating the resulting waveform into the model, they aim to mitigate the dullness and ringing artifacts, thus improving the overall quality of the converted singing voice.
The second method focuses on enhancing concatenative smoothness, which is a key perceptual factor in SVC. Concatenative smoothness refers to the seamless transition between different segments of the converted voice. The authors propose a new distance metric that filters out unsuitable kNN candidates and optimizes the summing weights of the candidates during inference. This approach aims to improve the smoothness of the converted singing voice by selecting appropriate candidates and optimizing their contributions.
It is worth noting that while these techniques are developed within the kNN-VC framework, they have broader applicability to general concatenative neural synthesis models. This highlights the potential for these methods to be employed in various other voice conversion systems beyond kNN-VC.
The paper also presents experimental results that validate the effectiveness of these modifications in achieving robust SVC. The authors provide a demo of their system, accessible at http://knnsvc.com, allowing users to experience the improvements firsthand. Additionally, the source code for their implementation is available on GitHub at https://github.com/SmoothKen/knn-svc, enabling researchers and developers to replicate and build upon their work.
In summary, this research introduces valuable enhancements to the kNN-VC framework for SVC by addressing issues related to dullness, ringing artifacts, and concatenative smoothness. The proposed methods demonstrate promising results and have the potential to be applied in other concatenative neural synthesis models, paving the way for further advancements in singing voice conversion technology.
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by jsendak | Apr 9, 2025 | Art
Potential Future Trends in Art Exhibition: Analyzing Madison Skriver’s After Dark
Enari Gallery is pleased to unveil its latest solo exhibition, entitled “After Dark,” featuring the works of artist Madison Skriver. Skriver’s new series takes a deep dive into the interplay between nostalgia and reality, drawing inspiration from mid-century American culture and cinematic storytelling. This article will analyze the key points of the exhibition and provide insights into the potential future trends that may arise in the art industry, along with my own predictions and recommendations.
Exploring the Tension Between Nostalgia and Reality
Skriver’s artwork in “After Dark” addresses the timeless struggle between our nostalgic yearning for the past and the disquieting truths concealed beneath the idealized facade of mid-century American culture. Drawing influence from renowned filmmaker David Lynch, the artist skillfully highlights the stark contrast between the seemingly perfect American dream and the unsettling realities that lie beneath its glossy surface.
Through her bold use of colors, surreal light techniques, and layered symbolism, Skriver creates a mesmerizing dreamlike atmosphere that effectively encapsulates the haunting sense of the past. The exhibition invites viewers to confront the duality of nostalgia, sparking conversations about our collective desire to romanticize a bygone era while acknowledging the challenging aspects obscured within it.
Potential Future Trends
- Nostalgia Renaissance: Skriver’s exploration of nostalgia and reality resonates with contemporary audiences. This exhibition reflects a growing trend of embracing nostalgia in art, pop culture, and design. As society becomes increasingly fast-paced and uncertain, people yearn for the comfort and familiarity of the past. Artists who can evoke nostalgia while also challenging its idealized notion are likely to capture the attention of future audiences.
- Blurring Boundaries: Skriver’s fusion of mid-century American culture with cinematic storytelling showcases the potential for artists to break traditional boundaries in their work. As technology continues to evolve, artists are no longer confined to a specific medium or style. They can experiment with various techniques, combining elements from different eras and art forms to create thought-provoking and visually striking pieces. This trend of boundary-breaking art is likely to gain traction in the coming years.
- Social Commentary: “After Dark” highlights the power of art to provoke important discussions about societal issues. In the future, we can expect more artists to leverage their work as a platform for social and cultural commentary. By addressing the unsettling truths beneath the surface of nostalgia and the American dream, Skriver inspires viewers to critically engage with the complexities of our society. Artists who use their talent to shed light on pressing matters are likely to make a significant impact on the art world.
Predictions and Recommendations
Based on the analysis of Madison Skriver’s “After Dark” and the potential future trends, the following predictions and recommendations can be made:
- Embrace Multidisciplinary Approaches: Artists should experiment with various mediums, techniques, and art forms to create innovative and boundary-breaking pieces. By blurring traditional boundaries, artists can capture the interest of a broader audience and make a lasting impact on the art world.
- Create Thought-Provoking Narratives: Artists should strive to convey deeper messages and provoke critical thinking through their work. By tackling important societal issues, artists can effectively use their creations as a medium for social commentary, engaging viewers and fostering meaningful conversations.
- Balance Nostalgia with Nuanced Realism: Nostalgia will continue to have a significant influence on art, but it is crucial for artists to strike a balance between nostalgia and a nuanced reflection of reality. By challenging the idealized versions of the past and introducing elements that prompt introspection, artists can evoke a more profound emotional response from their audience.
References:
Enari Gallery. After Dark Exhibition Catalogue.
David Lynch’s Influence on Contemporary Art: Auteur and Avant-Garde. (n.d.). Retrieved from [insert reference here]
In conclusion, Madison Skriver’s “After Dark” exhibition serves as a strong indicator of potential future trends in the art industry. By exploring the interplay between nostalgia and reality, the exhibition taps into the growing fascination with nostalgia, the need for boundary-breaking art, and the power of social commentary. Artists who embrace multidisciplinary approaches, create thought-provoking narratives, and strike a balance between nostalgia and nuanced realism are likely to thrive in the ever-evolving art landscape. “After Dark” stands as a testament to the enduring power of art to challenge, inspire, and spark meaningful conversations among viewers.
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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