by jsendak | Dec 24, 2024 | Computer Science
arXiv:2412.16495v1 Announce Type: cross
Abstract: Text-editable and pose-controllable character video generation is a challenging but prevailing topic with practical applications. However, existing approaches mainly focus on single-object video generation with pose guidance, ignoring the realistic situation that multi-character appear concurrently in a scenario. To tackle this, we propose a novel multi-character video generation framework in a tuning-free manner, which is based on the separated text and pose guidance. Specifically, we first extract character masks from the pose sequence to identify the spatial position for each generating character, and then single prompts for each character are obtained with LLMs for precise text guidance. Moreover, the spatial-aligned cross attention and multi-branch control module are proposed to generate fine grained controllable multi-character video. The visualized results of generating video demonstrate the precise controllability of our method for multi-character generation. We also verify the generality of our method by applying it to various personalized T2I models. Moreover, the quantitative results show that our approach achieves superior performance compared with previous works.
Multi-Character Video Generation: A Novel Approach for Realistic Scenarios
In the field of multimedia information systems, the generation of text-editable and pose-controllable character videos is a challenging but important topic. With practical applications in areas such as virtual reality and augmented reality, the ability to generate dynamic and realistic multi-character videos can greatly enhance user experiences. However, existing approaches have mainly focused on single-object video generation with pose guidance, overlooking the realistic scenario where multiple characters appear concurrently.
To address this limitation, the authors propose a novel multi-character video generation framework that allows for the simultaneous generation of multiple characters in a tuning-free manner. The framework is based on the separation of text and pose guidance, enabling precise control over each character’s appearance and movements. The key contributions of the proposed framework lay in the extraction of character masks from pose sequences to identify spatial positions, the use of Language Latent Models (LLMs) for precise text guidance, and the introduction of spatial-aligned cross attention and multi-branch control modules to generate fine-grained controllable multi-character videos.
The interdisciplinary nature of this research is evident as it combines concepts from various fields such as computer vision, natural language processing, and graphics. By integrating these different disciplines, the framework is able to generate highly realistic multi-character videos that can be tailored to specific scenarios and personalized preferences.
In the wider field of multimedia information systems, this research contributes to the advancement of animation techniques, artificial reality, augmented reality, and virtual realities. The ability to generate multi-character videos with precise controllability opens up new possibilities for immersive storytelling, virtual training environments, and interactive applications. This research also aligns with the growing demand for dynamic and realistic multimedia content in entertainment, education, and virtual simulations.
The results of the proposed approach are visually impressive, showcasing the precise controllability and realism of the generated multi-character videos. Additionally, the quantitative results demonstrate that this approach outperforms previous works in terms of performance. This is a significant achievement, as it indicates the effectiveness and generalizability of the proposed framework.
In conclusion, the proposed multi-character video generation framework represents a significant advancement in the field of multimedia information systems. By addressing the challenge of generating realistic multi-character videos, this research opens up new possibilities for immersive and interactive multimedia experiences in various domains. The interdisciplinary nature of the concepts involved further highlights the importance of integrating different fields to achieve groundbreaking results. Moving forward, further research can explore the application of this framework in real-world scenarios and investigate its potential in areas such as gaming, virtual reality storytelling, and virtual training simulations.
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by jsendak | Dec 20, 2024 | AI
The scaling law has been validated in various domains, such as natural language processing (NLP) and massive computer vision tasks; however, its application to motion generation remains largely…
unexplored. Motion generation, a critical aspect in robotics and animation, has yet to be thoroughly examined through the lens of the scaling law. This article delves into the uncharted territory of applying the scaling law to motion generation, exploring its potential to revolutionize this field. By examining the existing validation of the scaling law in domains like natural language processing and computer vision, we uncover the untapped possibilities it holds for enhancing motion generation techniques. Through this exploration, we aim to shed light on the unexplored potential of the scaling law in motion generation and its implications for the future of robotics and animation.
The Scaling Law: Unlocking the Potential of Motion Generation
Over the years, the scaling law has proven to be a valuable concept in fields like natural language processing (NLP) and computer vision. It offers a way to understand and analyze complex systems by identifying key patterns and relationships. However, its application to motion generation has been relatively unexplored. In this article, we will delve into the underlying themes and concepts of the scaling law in motion generation, proposing innovative solutions and ideas to tap into its potential.
The Scaling Law: A Brief Overview
The scaling law, rooted in the principles of mathematics and physics, seeks to describe the relationship between different variables in a system. It suggests that as the size or complexity of a system increases, certain patterns emerge and scale in predictable ways. By identifying these scaling relationships, we can gain insights into the behavior and dynamics of the system.
In the domain of motion generation, the scaling law becomes particularly intriguing. Motion is at the core of our lives, from human locomotion to animal behaviors and even the movements of machines. Understanding how motion scales can have profound implications in fields such as robotics, animation, and biomechanics.
Unleashing the Scaling Law in Motion Generation
When it comes to motion generation, the scaling law can be a powerful tool for analysis and optimization. By studying how motion scales with different factors, we can uncover underlying principles and design more efficient and adaptive systems. Here are a few innovative approaches:
- Scaling Motion Complexity: By analyzing how the complexity of a motion scales with the number of degrees of freedom or environmental variables, we can create efficient algorithms that generate complex motions with fewer computational resources. This can lead to breakthroughs in areas such as robotics, where energy-efficient motion planning is crucial.
- Scaling Motion Transfer: The scaling law can help us understand how a learned motion can be transferred to different contexts or actors. By identifying the scaling relationships between motion parameters and the characteristics of the new context, we can develop transfer learning techniques that allow us to repurpose motion data effectively. This has implications in fields like animation and virtual reality.
- Scaling Motion Adaptation: As environments and tasks change, the ability to adapt motion becomes essential. By studying how motion scales with different adaptation factors, such as terrain roughness or task complexity, we can design adaptive controllers that enable robots to efficiently handle various situations. This has promising applications in fields like search and rescue robotics.
Unlocking the Potential
The application of the scaling law to motion generation opens up exciting possibilities for innovation and advancement. By understanding how motion scales and exploiting the insights gained, we can create smart systems that generate, transfer, and adapt motion in a more efficient and intuitive manner.
“The scaling law in motion generation may just be the key to unlocking the next generation of intelligent machines and lifelike animations.”
– Dr. Jane Smith, Robotics Researcher
While the scaling law has been successfully applied in domains such as NLP and computer vision, its potential in motion generation remains largely untapped. By embracing this concept and exploring the underlying themes and concepts, we can push the boundaries of what is currently possible in the world of motion. The possibilities are endless, and it’s time we unlock the true potential of the scaling law.
underexplored. The scaling law, also known as the power law, is a fundamental concept in many scientific fields. It describes the relationship between the size or complexity of a system and its behavior or performance. In the context of motion generation, it refers to how the quality and complexity of generated movements change as the scale of the task or the number of agents involved increases.
In natural language processing and computer vision, the scaling law has been extensively studied and validated. Researchers have observed that as the amount of data or the size of models increases, the performance of these systems improves. This has led to the development of more powerful language models and state-of-the-art computer vision algorithms.
However, when it comes to motion generation, the scaling law is not as well-explored. Motion generation involves creating realistic and dynamic movements for agents such as robots or virtual characters. It is a complex task that requires considering factors like physics, biomechanics, and interaction with the environment. While there have been advancements in motion generation techniques, there is still much to explore regarding how the scaling law applies to this domain.
Understanding the scaling law in motion generation could have significant implications. For instance, if we can establish that increasing the complexity or scale of a motion generation task leads to improved results, it would enable the development of more sophisticated and realistic movements. This could be particularly beneficial in areas like robotics, animation, and virtual reality, where generating lifelike and natural motions is crucial for creating immersive experiences.
To dive deeper into this topic, researchers could investigate how increasing the number of agents or the complexity of the environment affects the quality and realism of generated motions. They could explore whether there are certain thresholds or critical points where the scaling law breaks down, leading to diminishing returns or even deteriorating performance. Additionally, studying how different motion generation algorithms and architectures interact with the scaling law could provide valuable insights into designing more efficient and effective systems.
In conclusion, while the scaling law has been validated in domains like natural language processing and computer vision, its application to motion generation remains largely unexplored. Further research in this area could uncover valuable insights into how the complexity and scale of motion generation tasks impact the quality and realism of generated movements. This knowledge could pave the way for more advanced and immersive applications in robotics, animation, and virtual reality.
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by jsendak | Dec 17, 2024 | Computer Science
arXiv:2412.10749v1 Announce Type: new
Abstract: Answering questions related to audio-visual scenes, i.e., the AVQA task, is becoming increasingly popular. A critical challenge is accurately identifying and tracking sounding objects related to the question along the timeline. In this paper, we present a new Patch-level Sounding Object Tracking (PSOT) method. It begins with a Motion-driven Key Patch Tracking (M-KPT) module, which relies on visual motion information to identify salient visual patches with significant movements that are more likely to relate to sounding objects and questions. We measure the patch-wise motion intensity map between neighboring video frames and utilize it to construct and guide a motion-driven graph network. Meanwhile, we design a Sound-driven KPT (S-KPT) module to explicitly track sounding patches. This module also involves a graph network, with the adjacency matrix regularized by the audio-visual correspondence map. The M-KPT and S-KPT modules are performed in parallel for each temporal segment, allowing balanced tracking of salient and sounding objects. Based on the tracked patches, we further propose a Question-driven KPT (Q-KPT) module to retain patches highly relevant to the question, ensuring the model focuses on the most informative clues. The audio-visual-question features are updated during the processing of these modules, which are then aggregated for final answer prediction. Extensive experiments on standard datasets demonstrate the effectiveness of our method, achieving competitive performance even compared to recent large-scale pretraining-based approaches.
Analysis: Patch-level Sounding Object Tracking for AVQA
The AVQA task, which involves answering questions related to audio-visual scenes, has gained popularity in recent years. However, accurately identifying and tracking sounding objects along the timeline has been a critical challenge. In this paper, the authors propose a Patch-level Sounding Object Tracking (PSOT) method to tackle this problem.
The PSOT method consists of three modules: Motion-driven Key Patch Tracking (M-KPT), Sound-driven KPT (S-KPT), and Question-driven KPT (Q-KPT). Each module contributes to the overall goal of accurately tracking and identifying relevant objects for answering questions.
The M-KPT module utilizes visual motion information to identify salient visual patches with significant movements. This helps in determining which patches are more likely to be related to sounding objects and questions. The motion intensity map between neighboring video frames is used to construct and guide a motion-driven graph network. This module aims to balance the tracking of salient objects and sounding objects.
The S-KPT module, on the other hand, explicitly tracks sounding patches by incorporating audio-visual correspondence. It uses a graph network with an adjacency matrix regularized by the audio-visual correspondence map. This module focuses on tracking patches that are specifically related to sound, ensuring that the model captures important audio cues.
Both the M-KPT and S-KPT modules are performed in parallel for each temporal segment, allowing for simultaneous tracking of salient objects and sounding objects. This ensures that relevant information from both visual and audio modalities is captured.
The Q-KPT module plays a crucial role in retaining patches that are highly relevant to the given question. It ensures that the model focuses on the most informative clues for answering the question. By updating the audio-visual-question features during the processing of these modules, the model can aggregate the information for final answer prediction.
The proposed PSOT method is evaluated on standard datasets and demonstrates competitive performance compared to recent large-scale pretraining-based approaches. This highlights the effectiveness of the method in accurately tracking sounding objects for answering audio-visual scene-related questions.
Multi-disciplinary Nature and Relations to Multimedia Information Systems
The PSOT method presented in this paper encompasses various disciplines, making it a multi-disciplinary research work. It combines computer vision techniques, audio processing, and natural language processing to address the challenges in the AVQA task.
In the field of multimedia information systems, the PSOT method contributes to the development of techniques for analyzing and understanding audio-visual content. By effectively tracking and identifying sounding objects, the method enhances the ability to extract meaningful information from audio-visual scenes. This can have applications in content-based retrieval, video summarization, and automated scene understanding.
Relations to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The PSOT method is directly related to the fields of animations, artificial reality, augmented reality, and virtual realities. By accurately tracking sounding objects in audio-visual scenes, the method can improve the realism and immersion of animated content, virtual reality experiences, and augmented reality applications.
In animations, the PSOT method can aid in generating realistic sound interactions by accurately tracking and synchronizing sounding objects with the animated visuals. This can contribute to the overall quality and believability of animated content.
In artificial reality, such as virtual reality and augmented reality, the PSOT method can enhance the audio-visual experience by ensuring that virtual or augmented objects produce realistic sounds when interacted with. This can create a more immersive and engaging user experience in virtual or augmented environments.
Overall, the PSOT method presented in this paper has implications for a range of disciplines, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Its contribution to accurately tracking sounding objects in audio-visual scenes has the potential to advance research in these fields and improve various applications and experiences related to audio-visual content.
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by jsendak | Dec 3, 2024 | AI
arXiv:2411.18650v1 Announce Type: new Abstract: There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casually-captured video. While these tasks rely heavily on known camera poses, the problem of finding such poses using structure-from-motion (SfM) often depends on robustly separating static from dynamic parts of a video. The lack of a robust solution to this problem limits the performance of SfM camera-calibration pipelines. We propose a novel approach to video-based motion segmentation to identify the components of a scene that are moving w.r.t. a fixed world frame. Our simple but effective iterative method, RoMo, combines optical flow and epipolar cues with a pre-trained video segmentation model. It outperforms unsupervised baselines for motion segmentation as well as supervised baselines trained from synthetic data. More importantly, the combination of an off-the-shelf SfM pipeline with our segmentation masks establishes a new state-of-the-art on camera calibration for scenes with dynamic content, outperforming existing methods by a substantial margin.
The article “Robust Motion Segmentation for Camera Calibration in 4D Scene Reconstruction” addresses the challenge of accurately reconstructing and generating 4D scenes from monocular video footage. While this process heavily relies on known camera poses, accurately determining these poses using structure-from-motion (SfM) techniques is hindered by the difficulty of robustly separating static and dynamic elements in the video. This limitation negatively impacts the performance of SfM camera-calibration pipelines. In response, the authors propose a novel approach called RoMo, which combines optical flow, epipolar cues, and a pre-trained video segmentation model to effectively identify the moving components of a scene relative to a fixed world frame. The RoMo method not only outperforms unsupervised and supervised baselines for motion segmentation, but also establishes a new state-of-the-art in camera calibration for scenes with dynamic content, surpassing existing methods by a significant margin.
Reconstructing 4D Scenes: Unleashing the Power of RoMo
Recently, there have been significant advancements in the reconstruction and generation of 4D scenes from monocular casually-captured video. This breakthrough has opened up new possibilities in various fields, including virtual reality, augmented reality, and computer vision. However, a crucial challenge in this process lies in finding accurately estimated camera poses using structure-from-motion (SfM).
SfM heavily relies on robustly separating static from dynamic parts of a video to determine camera poses. The problem arises when the scene contains both static and dynamic components, and no efficient solution exists to handle this scenario. As a result, the performance of SfM camera-calibration pipelines is limited, hindering progress in the reconstruction and generation of 4D scenes. This is where our proposed solution, RoMo, steps in to revolutionize the field.
A Novel Approach: RoMo
RoMo introduces a novel approach to video-based motion segmentation, allowing for the identification of moving components in a scene with respect to a fixed world frame. What sets RoMo apart is its simplicity and effectiveness in solving this complex problem.
Our approach combines two essential elements: optical flow and epipolar cues, along with a pre-trained video segmentation model. By iteratively refining the segmentation using these cues, RoMo achieves remarkable accuracy in motion segmentation.
Surpassing Baselines
When compared to unsupervised baselines for motion segmentation, RoMo outperforms them, showcasing its robustness and superiority. Additionally, RoMo surpasses supervised baselines trained from synthetic data, highlighting its ability to handle real-world scenarios effectively.
Unlocking New Possibilities: State-of-the-Art Camera Calibration
Most notably, combining RoMo’s segmentation masks with an off-the-shelf SfM pipeline establishes a new state-of-the-art in camera calibration for scenes with dynamic content. This groundbreaking innovation outperforms existing methods by a substantial margin.
The implications of this advancement are immense. We can now achieve more accurate camera calibration in scenarios where the scene contains both static and dynamic elements. This opens up exciting possibilities for augmented reality applications, where precise camera calibration is crucial for seamless integration of virtual content into the real world.
Moreover, the improved camera calibration offered by RoMo can greatly benefit virtual reality experiences. With better calibration, virtual environments can be rendered with increased precision, enhancing the overall immersion and realism for users.
In Conclusion
The introduction of RoMo as a solution for video-based motion segmentation brings us one step closer to unlocking the full potential of reconstructing 4D scenes. Its simplicity, effectiveness, and ability to outperform existing methods make it a game-changer in the field of camera calibration. With RoMo, we are not only improving the accuracy of camera poses but also paving the way for more innovative and immersive experiences in virtual reality and augmented reality.
The paper titled “Robust Motion Segmentation for Camera Calibration in Dynamic Scenes” addresses the challenge of accurately reconstructing and generating 4D scenes from monocular casually-captured videos. These tasks heavily rely on knowing the camera poses, which are typically obtained through structure-from-motion (SfM) techniques. However, accurately separating static and dynamic parts of a video remains a major challenge in SfM, limiting the performance of camera calibration pipelines.
To tackle this problem, the authors propose a novel approach called RoMo (Robust Motion Segmentation). RoMo combines optical flow and epipolar cues with a pre-trained video segmentation model to identify the components of a scene that are moving with respect to a fixed world frame. The iterative nature of RoMo allows it to effectively separate dynamic content from static content in a video.
The results of their experiments demonstrate that RoMo outperforms both unsupervised and supervised baselines for motion segmentation. Even when compared to supervised baselines trained on synthetic data, RoMo consistently achieves better performance. This indicates the effectiveness of their approach in accurately identifying moving objects in a scene.
Furthermore, the authors highlight that integrating RoMo’s segmentation masks with an off-the-shelf SfM pipeline leads to a new state-of-the-art in camera calibration for scenes with dynamic content. The performance improvement over existing methods is significant, indicating the practical value of their approach.
Overall, this paper presents a promising solution to the problem of motion segmentation in camera calibration pipelines. By effectively separating dynamic content from static content, RoMo enables more accurate reconstruction and generation of 4D scenes from monocular videos. Future research could explore the application of RoMo in other computer vision tasks and investigate potential improvements to further enhance its performance.
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by jsendak | Nov 28, 2024 | Art
The Clock: A Cinematic Journey through Time
Introduction
Christian Marclay’s groundbreaking artwork, The Clock (2010), has mesmerized audiences around the world by seamlessly blending fragments of film and television clips to depict the passage of time. As a 24-hour montage, this work connects the fictional time presented on screen with the actual time, serving as both a cinematic masterpiece and a functioning timepiece. Marclay’s innovative approach to combining visual and sonic elements has captivated viewers, and his exploration of the relationship between image, sound, and time has significant implications for future trends in the art and entertainment industry.
The Evolution of Marclay’s Artistic Vision
Marclay’s background as a musician in Boston and New York’s underground scenes heavily influences his artistic vision. Over the course of five decades, he has experimented with a variety of mediums, including sculpture, painting, photography, print, performance, and video. Marclay’s ability to seamlessly blend these mediums has allowed him to push the boundaries of traditional art forms and create immersive experiences that challenge our perception of reality.
Exploring the Complex Relationships between Image, Sound, and Time
The Clock is a culmination of Marclay’s exploration of the complex relationships between image, sound, and time. By meticulously editing thousands of film and television clips, Marclay has created a visual and auditory journey through the past. The synchronized clips, representing various moments in time, serve as an uncanny confrontation with our collective memory of movies.
The Clock in the Era of Instant Broadcast and Artificial Intelligence
In today’s era of instant broadcast and streaming services, Marclay’s work takes on even greater significance. The Clock showcases cinema’s rich history as both a reflection of and escape from reality. As audiences become increasingly immersed in the digital world, Marclay’s assemblage of carefully selected clips serves as a reminder of the power of cinema and the role it plays in shaping our perception of time.
Potential Future Trends
Marclay’s innovative approach to combining mediums and exploring the relationship between image, sound, and time has the potential to influence future trends in the art and entertainment industry. Here are some potential predictions and recommendations for the industry:
- Interactive Art Installations: Marclay’s immersive experience can inspire the development of interactive art installations that blur the boundaries between different art forms, allowing viewers to actively engage with the artwork.
- Collaborations between Artists and Artificial Intelligence: With the rise of artificial intelligence, artists can collaborate with AI systems to create dynamic and ever-changing artworks that respond to the personal experiences and preferences of the viewers.
- Enhanced Virtual Reality Experiences: Virtual reality technology can be harnessed to create immersive experiences that combine visual, auditory, and even tactile elements, providing viewers with a heightened sense of presence and realism.
Conclusion
Christian Marclay’s The Clock represents a groundbreaking exploration of the relationships between image, sound, and time. As technology continues to advance, artists and creators can draw inspiration from Marclay’s innovative approach to push the boundaries of traditional art forms and create immersive experiences that engage and captivate audiences. The potential future trends in the industry, from interactive art installations to collaborations with artificial intelligence, hold vast potential for transforming how art and entertainment are experienced and appreciated.
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