by jsendak | Jan 11, 2024 | AI
We propose DiffSHEG, a Diffusion-based approach for Speech-driven Holistic 3D
Expression and Gesture generation with arbitrary length. While previous works
focused on co-speech gesture or expression generation individually, the joint
generation of synchronized expressions and gestures remains barely explored. To
address this, our diffusion-based co-speech motion generation transformer
enables uni-directional information flow from expression to gesture,
facilitating improved matching of joint expression-gesture distributions.
Furthermore, we introduce an outpainting-based sampling strategy for arbitrary
long sequence generation in diffusion models, offering flexibility and
computational efficiency. Our method provides a practical solution that
produces high-quality synchronized expression and gesture generation driven by
speech. Evaluated on two public datasets, our approach achieves
state-of-the-art performance both quantitatively and qualitatively.
Additionally, a user study confirms the superiority of DiffSHEG over prior
approaches. By enabling the real-time generation of expressive and synchronized
motions, DiffSHEG showcases its potential for various applications in the
development of digital humans and embodied agents.
DiffSHEG (Diffusion-based approach for Speech-driven Holistic 3D Expression and Gesture generation) is a groundbreaking method that combines speech, expression, and gesture generation. While previous studies have focused on generating co-speech gestures or expressions separately, the joint generation of synchronized expressions and gestures has been underexplored. DiffSHEG addresses this gap by introducing a diffusion-based co-speech motion generation transformer, which allows for improved matching of joint expression-gesture distributions.
What sets DiffSHEG apart is its uni-directional information flow from expression to gesture. By enabling expression to influence gesture generation, DiffSHEG ensures a more coherent and synchronized output. This multi-disciplinary approach draws insights from speech processing, computer vision, and motion generation. By integrating these disciplines, DiffSHEG creates a holistic system that generates expressive and synchronized motions in real-time.
One of the key contributions of DiffSHEG is the outpainting-based sampling strategy for generating arbitrary long sequences in diffusion models. This strategy offers flexibility and computational efficiency, allowing for the practical application of DiffSHEG in various scenarios. Furthermore, the method has been evaluated on two public datasets, demonstrating state-of-the-art performance both quantitatively and qualitatively.
To validate the effectiveness of DiffSHEG, a user study was conducted, confirming its superiority over prior approaches. This user study further reaffirms the potential of DiffSHEG for applications in the development of digital humans and embodied agents.
The significance of DiffSHEG lies in its ability to generate high-quality synchronized expression and gesture generation driven by speech. This technology opens doors for applications in human-computer interaction, virtual reality, animation, and robotics. By seamlessly integrating speech, expression, and gesture generation, DiffSHEG paves the way for more natural and immersive interactions between humans and machines.
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by jsendak | Jan 10, 2024 | Computer Science
There has been a growing interest in the task of generating sound for silent
videos, primarily because of its practicality in streamlining video
post-production. However, existing methods for video-sound generation attempt
to directly create sound from visual representations, which can be challenging
due to the difficulty of aligning visual representations with audio
representations. In this paper, we present SonicVisionLM, a novel framework
aimed at generating a wide range of sound effects by leveraging vision language
models. Instead of generating audio directly from video, we use the
capabilities of powerful vision language models (VLMs). When provided with a
silent video, our approach first identifies events within the video using a VLM
to suggest possible sounds that match the video content. This shift in approach
transforms the challenging task of aligning image and audio into more
well-studied sub-problems of aligning image-to-text and text-to-audio through
the popular diffusion models. To improve the quality of audio recommendations
with LLMs, we have collected an extensive dataset that maps text descriptions
to specific sound effects and developed temporally controlled audio adapters.
Our approach surpasses current state-of-the-art methods for converting video to
audio, resulting in enhanced synchronization with the visuals and improved
alignment between audio and video components. Project page:
https://yusiissy.github.io/SonicVisionLM.github.io/
Analysis: SonicVisionLM – Generating Sound for Silent Videos
Generating sound for silent videos has gained significant interest in recent years due to its practicality in streamlining video post-production. However, existing methods face challenges in aligning visual representations with audio representations. In this paper, the authors propose SonicVisionLM, a novel framework that leverages vision language models (VLMs) to generate a wide range of sound effects.
The adoption of VLMs in SonicVisionLM represents a multi-disciplinary approach that combines computer vision and natural language processing. By using VLMs, the framework is able to identify events within a silent video and suggest relevant sounds that match the visual content. This shift in approach simplifies the complex task of aligning image and audio, transforming it into more well-studied sub-problems of aligning image-to-text and text-to-audio.
These sub-problems are addressed through the use of diffusion models, which have been widely used in the field of multimedia information systems. Diffusion models facilitate the process of converting text descriptions into specific sound effects. Additionally, the authors have developed temporally controlled audio adapters to improve the quality of audio recommendations with VLMs. This integration of different techniques enhances the overall synchronization between audio and video components.
With the proposed SonicVisionLM framework, the authors have surpassed current state-of-the-art methods for converting video to audio. They have achieved enhanced synchronization with visuals and improved alignment between audio and video components. By utilizing VLMs and diffusion models, the framework demonstrates the potential of combining various disciplines to advance the field of multimedia information systems. This research opens up possibilities for further exploration and development of advanced techniques in animations, artificial reality, augmented reality, and virtual realities.
For more details and access to the project page, please visit: https://yusiissy.github.io/SonicVisionLM.github.io/
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by jsendak | Jan 8, 2024 | AI
With the rapid development of imaging sensor technology in the field of
remote sensing, multi-modal remote sensing data fusion has emerged as a crucial
research direction for land cover classification tasks. While diffusion models
have made great progress in generative models and image classification tasks,
existing models primarily focus on single-modality and single-client control,
that is, the diffusion process is driven by a single modal in a single
computing node. To facilitate the secure fusion of heterogeneous data from
clients, it is necessary to enable distributed multi-modal control, such as
merging the hyperspectral data of organization A and the LiDAR data of
organization B privately on each base station client. In this study, we propose
a multi-modal collaborative diffusion federated learning framework called
FedDiff. Our framework establishes a dual-branch diffusion model feature
extraction setup, where the two modal data are inputted into separate branches
of the encoder. Our key insight is that diffusion models driven by different
modalities are inherently complementary in terms of potential denoising steps
on which bilateral connections can be built. Considering the challenge of
private and efficient communication between multiple clients, we embed the
diffusion model into the federated learning communication structure, and
introduce a lightweight communication module. Qualitative and quantitative
experiments validate the superiority of our framework in terms of image quality
and conditional consistency.
Analysis of Multi-Modal Collaborative Diffusion Federated Learning
The rapid development of imaging sensor technology in remote sensing has paved the way for multi-modal remote sensing data fusion. This approach is crucial for accurate land cover classification tasks, as it combines information from different sensors to produce more comprehensive and reliable results. However, existing models in this area have primarily focused on single-modality and single-client control.
One of the key challenges in enabling the secure fusion of heterogeneous data from clients is achieving distributed multi-modal control. This means allowing different clients to merge their private data on their own computing nodes without compromising privacy or security. To address this challenge, the authors propose a multi-modal collaborative diffusion federated learning framework called FedDiff.
The framework introduces a dual-branch diffusion model feature extraction setup, where each modality is inputted into separate branches of the encoder. The underlying insight is that diffusion models driven by different modalities are inherently complementary, allowing for potential denoising steps that can be leveraged through bilateral connections. This approach combines the strengths of each modality and enhances the overall performance of land cover classification tasks.
In addition to addressing the challenge of data fusion, the authors also consider the need for private and efficient communication between multiple clients. To achieve this, they embed the diffusion model into the federated learning communication structure and introduce a lightweight communication module. This ensures that sensitive data remains private while enabling efficient collaboration and knowledge sharing among clients.
In order to evaluate the performance of the proposed framework, qualitative and quantitative experiments were conducted. These experiments demonstrate the superiority of FedDiff in terms of image quality and conditional consistency. The framework shows promise in improving land cover classification tasks by leveraging the benefits of multi-modal data fusion and distributed collaboration.
Multi-Disciplinary Nature
This study touches upon various disciplines, highlighting the multi-disciplinary nature of the concepts presented. The fusion of remote sensing data requires knowledge and expertise in imaging sensor technology, computer vision, and machine learning. Additionally, the inclusion of federated learning and privacy-preserving communication techniques brings in concepts from distributed systems, cryptography, and data security. This interdisciplinary approach enhances the understanding of the challenges and opportunities in multi-modal remote sensing data fusion and provides a comprehensive solution to address them.
Potential Future Developments
The proposed FedDiff framework opens up possibilities for further research and development in the field of multi-modal collaborative diffusion federated learning. Here are a few potential areas that could be explored:
- Extension to additional modalities: The current framework focuses on two modalities, but future research could extend it to include more modalities, such as thermal or radar data, to further enhance land cover classification accuracy.
- Integration of more advanced diffusion models: While the proposed framework establishes a dual-branch diffusion model feature extraction setup, future work could investigate the integration of more advanced diffusion models, such as graph-based or attention-based models, to capture richer relationships between modalities.
- Addressing scalability challenges: As the number of clients and the size of their data increase, scalability becomes a significant concern. Future developments could focus on addressing scalability challenges, such as efficient aggregation algorithms and distributed computing strategies, to accommodate large-scale multi-modal federated learning scenarios.
- Exploring real-world applications: Applying the FedDiff framework to real-world land cover mapping applications can provide valuable insights into its practical effectiveness and potential limitations. Field experiments in different environmental and geographical contexts can help validate the framework’s generalizability and robustness.
In summary, the multi-modal collaborative diffusion federated learning framework presented in this study showcases the potential of leveraging distributed collaboration and fusion of heterogeneous data in land cover classification tasks. The multi-disciplinary nature of the concepts involved opens up opportunities for future research and development, pushing the boundaries of remote sensing and machine learning applications.
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by jsendak | Jan 8, 2024 | AI
Over the past several years, the synchronization between audio and visual
signals has been leveraged to learn richer audio-visual representations. Aided
by the large availability of unlabeled videos, many unsupervised training
frameworks have demonstrated impressive results in various downstream audio and
video tasks. Recently, Masked Audio-Video Learners (MAViL) has emerged as a
state-of-the-art audio-video pre-training framework. MAViL couples contrastive
learning with masked autoencoding to jointly reconstruct audio spectrograms and
video frames by fusing information from both modalities. In this paper, we
study the potential synergy between diffusion models and MAViL, seeking to
derive mutual benefits from these two frameworks. The incorporation of
diffusion into MAViL, combined with various training efficiency methodologies
that include the utilization of a masking ratio curriculum and adaptive batch
sizing, results in a notable 32% reduction in pre-training Floating-Point
Operations (FLOPS) and an 18% decrease in pre-training wall clock time.
Crucially, this enhanced efficiency does not compromise the model’s performance
in downstream audio-classification tasks when compared to MAViL’s performance.
In recent years, the synchronization of audio and visual signals has become a powerful tool for learning more comprehensive audio-visual representations. With the abundance of unlabeled videos available, unsupervised training frameworks have achieved impressive results in various audio and video tasks. Among these frameworks, Masked Audio-Video Learners (MAViL) has emerged as a leading pre-training framework, combining contrastive learning and masked autoencoding to reconstruct audio spectrograms and video frames. This paper explores the potential synergy between diffusion models and MAViL, aiming to derive mutual benefits from these two frameworks. By incorporating diffusion into MAViL and implementing training efficiency methodologies such as masking ratio curriculum and adaptive batch sizing, the authors achieve a significant reduction in pre-training Floating-Point Operations (FLOPS) by 32% and pre-training wall clock time by 18%. Importantly, this increased efficiency does not compromise the model’s performance in downstream audio-classification tasks compared to MAViL’s performance.
Exploring the Synergy Between Diffusion Models and MAViL: Enhancing Efficiency in Audio-Visual Pre-training
Over the past few years, the field of audio-visual representation learning has witnessed remarkable progress. By leveraging the synchronization between audio and visual signals, researchers have been able to extract richer information from unlabeled videos, leading to impressive results in various audio and video tasks. One such pre-training framework that has emerged as a state-of-the-art solution is Masked Audio-Video Learners (MAViL).
MAViL adopts a contrastive learning approach, combined with masked autoencoding, to reconstruct audio spectrograms and video frames. This fusion of information from both modalities enables the model to learn robust representations. However, there is still room for improvement in terms of efficiency.
In this paper, we propose exploring the potential synergy between diffusion models and MAViL to enhance the efficiency of audio-visual pre-training. Diffusion models have gained attention for their ability to capture complex relationships and generate high-quality samples from a given distribution. By incorporating diffusion into MAViL, we aim to derive mutual benefits from these two frameworks.
One of the key advantages of integrating diffusion models with MAViL is the significant reduction in pre-training Floating-Point Operations (FLOPS). By carefully designing the diffusion process, we can minimize the computational overhead while maintaining the quality of reconstructed audio spectrograms and video frames. Through experimentation, we achieved a notable 32% reduction in FLOPS compared to the original MAViL framework.
Additionally, our approach also addresses the issue of pre-training wall clock time. By adopting various training efficiency methodologies, such as a masking ratio curriculum and adaptive batch sizing, we were able to further optimize the training process. As a result, we observed an 18% decrease in pre-training wall clock time without compromising the model’s performance in downstream audio-classification tasks.
This enhanced efficiency is crucial for scaling up the deployment of MAViL and diffusion models in real-world applications. It allows researchers and practitioners to train larger models on larger datasets within a reasonable time frame, facilitating faster experimentation and advancements in audio-visual tasks. The reduced computational and time requirements make these frameworks more accessible and applicable to a wider range of projects.
In conclusion, the incorporation of diffusion models into MAViL brings about significant improvements in efficiency without compromising performance. By leveraging the strengths of both frameworks, we achieve a more streamlined audio-visual pre-training process. This paves the way for future research and innovation in the field, opening up new possibilities for advanced audio and video analysis applications.
The paper discusses the potential synergy between diffusion models and the MAViL (Masked Audio-Video Learners) framework, with the goal of deriving mutual benefits from these two approaches. MAViL is a state-of-the-art audio-video pre-training framework that combines contrastive learning with masked autoencoding to reconstruct audio spectrograms and video frames by leveraging information from both modalities.
The integration of diffusion models into MAViL, along with the implementation of various training efficiency methodologies, has resulted in significant improvements. The authors report a remarkable 32% reduction in pre-training Floating-Point Operations (FLOPS), which indicates a more efficient utilization of computational resources. Additionally, there is an 18% decrease in pre-training wall clock time, indicating a reduction in the overall time required for pre-training.
One key aspect of this study is that despite the increased efficiency, the performance of the model in downstream audio-classification tasks remains on par with MAViL’s original performance. This suggests that the incorporation of diffusion models and the implementation of efficiency methodologies have not compromised the model’s ability to learn and represent audio-visual information effectively.
This research is significant as it addresses the need for efficient pre-training frameworks that can leverage large amounts of unlabeled video data. By reducing computational requirements and training time without sacrificing performance, the proposed integration of diffusion models into MAViL holds promise for advancing audio-visual representation learning.
Looking ahead, it would be interesting to explore how this enhanced efficiency translates to other downstream tasks beyond audio-classification. Additionally, further investigation into the specific mechanisms through which diffusion models contribute to MAViL’s performance improvements could provide valuable insights for future research in this area. Overall, this study represents a promising step towards more efficient and effective audio-visual representation learning.
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by jsendak | Jan 7, 2024 | AI
Retail photography imposes specific requirements on images. For instance, images may need uniform background colors, consistent model poses, centered products, and consistent lighting. Minor…
Retail photography imposes specific requirements on images, including uniform background colors, consistent model poses, centered products, and consistent lighting. These seemingly minor details play a crucial role in creating a visually appealing and professional-looking product catalog or online store. In this article, we will delve into the importance of meeting these requirements and explore how they contribute to enhancing the overall shopping experience for customers. We will also discuss the challenges that retail photographers face in achieving these standards and highlight some effective techniques and tools that can help streamline the process. Whether you are a professional photographer or a retailer looking to optimize your product imagery, this article will provide valuable insights into the core themes of retail photography and how to elevate your visual content to attract and engage customers.
Retail photography is an integral element in the e-commerce industry, as it serves as a visual representation of products to potential customers. However, this particular branch of photography comes with its own set of challenges, requiring photographers to adhere to specific requirements to ensure the images are effective in promoting the products. These requirements often include uniform background colors, consistent model poses, centered products, and consistent lighting.
Addressing Challenges in Retail Photography
While these requirements may seem straightforward, achieving a high level of consistency and uniformity can be a daunting task for photographers. Here, we explore some innovative solutions and ideas to help overcome these challenges and elevate retail photography to new heights:
1. Embrace Diversity in Model Poses
Traditionally, retail photography has focused on presenting models in specific and repetitive poses, aiming for consistency across product lines. However, this approach can result in stagnant and uninspiring visuals. To inject fresh and dynamic energy into retail photography, photographers can experiment with diverse model poses that better reflect real-life situations and engage consumers.
By capturing models in natural and authentic poses, photographers can create a sense of relatability and make the products appear more tangible to potential customers. This approach not only adds a touch of uniqueness to the images but also fosters a deeper connection between the audience and the products being showcased.
2. Shift Towards Personalized Lighting
Consistent lighting is one of the key requirements in retail photography, as it ensures that the products are accurately represented and easily comparable for consumers. However, this does not mean that photographers should limit themselves to a single lighting setup.
An innovative approach to lighting in retail photography involves exploring personalized lighting techniques for each product or product category. By tailoring the lighting setup to match the specific characteristics of the item being photographed, photographers can highlight its unique features and create captivating visual narratives.
For example, jewelry photography could benefit from delicate and focused lighting to enhance the sparkle and brilliance of gemstones, while clothing photography might require softer and more evenly distributed light to accurately represent fabric textures and colors. By adopting personalized lighting approaches, photographers can add depth and character to their images, elevating the overall impact of the product.
3. Breaking Free from Uniform Backgrounds
A common requirement in retail photography is the use of uniform background colors to ensure product consistency across an e-commerce platform. However, this can sometimes result in a visually monotonous experience for shoppers, particularly when browsing through multiple product pages.
An innovative solution to this challenge is to break free from uniform backgrounds and introduce contextual or lifestyle backgrounds that complement the product being photographed. By placing products in real-world settings or incorporating visually appealing backgrounds, photographers can create a more immersive and engaging experience for consumers.
For example, instead of a plain white background, a photograph of a cozy sweater could feature a model wearing it in a warm and inviting living room setting. This approach not only adds visual interest but also allows potential customers to envision themselves using or wearing the product in their everyday lives, increasing the likelihood of a purchase.
In Conclusion
Retail photography is no longer confined to rigid guidelines and repetitive visuals. By embracing diversity in model poses, shifting towards personalized lighting, and breaking free from uniform backgrounds, photographers can invigorate their images and capture the attention of consumers in new and innovative ways. These approaches not only enhance the visual appeal of the products but also create a more compelling and immersive shopping experience for customers, ultimately driving better conversions and increased sales in the e-commerce industry.
details such as these play a crucial role in presenting products in an appealing and professional manner. Retailers understand that high-quality images can significantly impact customer perception, engagement, and ultimately, sales.
Uniform background colors are essential in retail photography as they create a cohesive and visually pleasing product catalog or website. Whether it’s a pure white background or a specific brand color, consistency across all images helps maintain a professional and polished look. This consistency also allows customers to focus solely on the product itself without any distractions.
Consistent model poses add an element of predictability and familiarity to retail photography. By using the same poses for different products, retailers can establish a visual language that customers can easily recognize and relate to. This approach helps build trust and enables customers to envision themselves using or wearing the product. Additionally, consistent model poses make it easier for customers to compare different products side by side, aiding their decision-making process.
Centering products within the frame is another important aspect of retail photography. Placing the product in the center helps draw attention to it and ensures that it becomes the focal point of the image. It allows customers to easily evaluate the product’s details and features without any distractions. Moreover, centered products facilitate a clean and symmetrical composition, which enhances the overall aesthetic appeal.
Consistent lighting is crucial in retail photography to accurately represent the product’s colors, textures, and details. Lighting can dramatically impact how a product appears in photographs, affecting its perceived quality and desirability. By maintaining consistent lighting conditions across all images, retailers ensure that customers get an accurate representation of the product, eliminating any potential surprises when it arrives at their doorstep.
While these requirements may seem minor at first glance, they collectively contribute to creating a professional and consistent visual brand identity for retailers. Consistency in retail photography builds trust with customers and reinforces a sense of reliability and quality.
Looking ahead, advancements in technology will likely continue to shape the field of retail photography. Artificial intelligence (AI) and machine learning algorithms can assist in automating some of the processes involved, such as background removal or color correction. This can save time and resources for retailers, allowing them to focus more on creative aspects and strategic decision-making.
Furthermore, virtual reality (VR) and augmented reality (AR) technologies hold immense potential for revolutionizing retail photography. By allowing customers to virtually try on clothes or visualize products in their own space, these technologies can provide a more immersive and interactive shopping experience. This could lead to increased customer engagement, reduced returns, and ultimately, higher conversion rates.
In conclusion, retail photography imposes specific requirements that are crucial for creating a visually appealing and consistent brand identity. Uniform background colors, consistent model poses, centered products, and consistent lighting all contribute to building trust with customers and enhancing their shopping experience. As technology continues to evolve, retailers can expect further advancements that streamline processes and introduce innovative ways to engage customers through visual content.
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![Diffusion Models as Masked Audio-Video Learners. (arXiv:2310.03937v2 [cs.SD] UPDATED)](https://qubixity.net/wp-content/uploads/2024/01/diffusion-models-as-masked-audio-video-learners-arxiv2310-03937v2-cs-sd-updated-1080x675.jpg)
