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.
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. Read the original article
arXiv:2501.09782v1 Announce Type: cross
Abstract: 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 training innovative architectural designs on confined datasets. In this work, we investigate the impact of scaling up EHPS towards a family of generalist foundation models. 1) For data scaling, we perform a systematic investigation on 40 EHPS datasets, encompassing a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. Ultimately, we achieve diminishing returns at 10M training instances from diverse data sources. 2) For model scaling, we take advantage of vision transformers (up to ViT-Huge as the backbone) to study the scaling law of model sizes in EHPS. To exclude the influence of algorithmic design, we base our experiments on two minimalist architectures: SMPLer-X, which consists of an intermediate step for hand and face localization, and SMPLest-X, an even simpler version that reduces the network to its bare essentials and highlights significant advances in the capture of articulated hands. With big data and the large model, the foundation models exhibit strong performance across diverse test benchmarks and excellent transferability to even unseen environments. Moreover, our finetuning strategy turns the generalist into specialist models, allowing them to achieve further performance boosts. Notably, our foundation models consistently deliver state-of-the-art results on seven benchmarks such as AGORA, UBody, EgoBody, and our proposed SynHand dataset for comprehensive hand evaluation. (Code is available at: https://github.com/wqyin/SMPLest-X).
Expressive human pose and shape estimation (EHPS) is a fascinating field that involves capturing the movements and shapes of the human body, hands, and face. This technology has a wide range of applications, from animation and virtual reality to artificial reality and multimedia information systems.
In this article, the authors explore the potential of scaling up EHPS towards the development of generalist foundation models. Currently, state-of-the-art methods in EHPS are focused on training innovative architectural designs on specific datasets. However, this approach has limitations as a model trained on a single dataset may not be able to handle a wide range of scenarios.
To overcome this limitation, the authors perform a systematic investigation on 40 EHPS datasets, covering various scenarios. By analyzing and benchmarking these datasets, they optimize their training scheme and select datasets that lead to significant improvements in EHPS capabilities. The authors find that they achieve diminishing returns at around 10 million training instances, indicating the importance of diverse data sources.
In addition to data scaling, the authors also investigate model scaling using vision transformers as the backbone. By using minimalist architectures, they study the scaling law of model sizes in EHPS, excluding the influence of algorithmic design. They find that with big data and large models, the foundation models exhibit strong performance across diverse test benchmarks and can even transfer their knowledge to unseen environments.
Furthermore, the authors develop a finetuning strategy that turns the generalist foundation models into specialist models, allowing them to achieve further performance boosts. These foundation models consistently deliver state-of-the-art results on multiple benchmarks, including AGORA, UBody, EgoBody, and the authors’ proposed SynHand dataset for comprehensive hand evaluation. This highlights the effectiveness and versatility of the developed EHPS techniques.
The concepts explored in this article highlight the multi-disciplinary nature of EHPS. It involves aspects of computer vision, machine learning, artificial intelligence, animation, and virtual reality. The ability to accurately capture and estimate human pose and shape has tremendous potential in various fields, including entertainment, gaming, healthcare, and even robotics.
In the wider field of multimedia information systems, EHPS plays a crucial role in enhancing the realism and interactivity of digital content. Whether it’s creating lifelike animations, developing immersive virtual reality experiences, or enabling augmented reality applications, EHPS provides the foundation for realistic human representations. By scaling up EHPS and developing generalist foundation models, we can expect even more advanced and realistic multimedia systems in the future.
The future of the gaming industry is anticipated to experience several transformative trends. These trends encompass enhanced virtual reality (VR) technology, the rise of mobile gaming, the emergence of cloud gaming, and the increasing importance of esports. In this article, we will delve into each of these themes, their potential implications for the industry, and provide unique predictions and recommendations for stakeholders in the gaming world.
Enhanced Virtual Reality (VR) Technology
Virtual reality has already made significant strides in gaming, but the future holds even more exciting possibilities. Advanced VR technology will offer gamers a more immersive and lifelike experience, blurring the line between virtual and real worlds. As VR headsets become more affordable and accessible, we can expect a surge in demand for VR games and applications.
In the near future, VR gaming is predicted to reach new heights by incorporating innovative features such as haptic feedback suits, which provide physical sensations to enhance the sense of presence. This technology will allow players to feel the impact of in-game actions, such as a punch or a collision, adding a new layer of realism to the gaming experience.
Predictions:
The demand for VR games will continue to rise, leading to an increase in the development of VR-exclusive titles.
VR arcades will become a popular destination as people seek out premium VR experiences that they may not have access to at home.
Multiplayer VR games will gain popularity, fostering a new social gaming experience where players can interact with each other in virtual worlds.
Recommendations:
Game developers should invest in VR game development and explore ways to create unique and engaging experiences that fully utilize the capabilities of VR technology.
VR hardware manufacturers should focus on improving affordability and accessibility to encourage wider adoption among gamers.
Gaming venues should consider incorporating VR arcades to cater to the growing demand for premium VR experiences.
The Rise of Mobile Gaming
Mobile gaming has already emerged as a dominant force in the gaming industry, and its prominence is set to grow even further in the future. The increasing power and capabilities of smartphones, coupled with the convenience of gaming on the go, have contributed to the widespread popularity of mobile games.
As technology continues to advance, mobile devices will become even more capable of delivering high-quality gaming experiences. The future of mobile gaming will see more graphically demanding games, console-like controls, and seamless integration with other devices and platforms.
Predictions:
The mobile gaming market will surpass the console and PC gaming markets in terms of revenue and player base.
Cloud gaming services will play a pivotal role in delivering console-quality games to mobile devices, eliminating the need for high-end hardware.
Augmented reality (AR) will become a standard feature in mobile games, enabling players to merge digital experiences with the real world.
Recommendations:
Mobile game developers should focus on creating high-quality games with engaging gameplay and visually stunning graphics.
Companies should invest in cloud gaming technologies to provide a seamless gaming experience on mobile devices.
Marketers should consider incorporating AR features in mobile game advertisements to create interactive and immersive promotional campaigns.
The Emergence of Cloud Gaming
Cloud gaming, also known as gaming on-demand, is poised to revolutionize the gaming industry. This technology allows players to stream games directly to their devices without the need for high-end hardware. The emergence of cloud gaming services like Google Stadia and Xbox Cloud Gaming (formerly known as Project xCloud) has paved the way for a future where games are accessible on any device with an internet connection.
With the infrastructure and internet speeds improving globally, cloud gaming is set to become more accessible and reliable. This trend will lead to a democratization of gaming, as players no longer need expensive gaming consoles or PCs to enjoy the latest titles.
Predictions:
Cloud gaming subscriptions will become the norm, with players having access to a vast library of games for a monthly fee.
Game developers will prioritize optimizing their games for cloud gaming platforms to reach a wider audience.
Hardware manufacturers may focus on producing dedicated cloud gaming devices that offer a seamless gaming experience.
Recommendations:
Gaming companies should collaborate with cloud gaming service providers to ensure their games are optimized for streaming.
Internet service providers should invest in expanding and improving their network infrastructure to support the increasing demand for cloud gaming.
Players should explore different cloud gaming services and choose the one that suits their gaming preferences and requirements.
The Increasing Importance of Esports
Esports, competitive video gaming, has gained significant traction in recent years and will continue to grow in importance in the future. Esports events now fill stadiums, attract millions of viewers online, and offer lucrative prize pools. As the esports industry matures, we can anticipate several developments and opportunities.
Esports will become even more mainstream, with increased coverage on traditional media platforms like television. The viewership base will expand, including not only avid gamers but also casual spectators who are drawn to the excitement and drama of competitive gaming.
Predictions:
Esports will be recognized as an official sport in more countries, leading to increased funding and support.
The esports industry will see further consolidation, with major companies acquiring or partnering with esports organizations and teams.
Esports betting will become more prevalent, creating new revenue streams and increasing viewer engagement.
Recommendations:
Brands and marketers should consider investing in esports sponsorships and partnerships to reach the highly engaged esports audience.
Esports organizations and teams should prioritize player welfare and mental health, ensuring sustainable and healthy environments for athletes.
Regulatory bodies and governments should formulate policies and regulations to protect both players and viewers in the esports industry.
In conclusion, the gaming industry is set for a future filled with enhanced virtual reality, mobile gaming dominance, cloud gaming revolution, and the rising significance of esports. Stakeholders in the gaming world should adapt to these trends and seize the opportunities they present. By embracing technology, focusing on quality and accessibility, and nurturing the esports ecosystem, the gaming industry can thrive and provide players with unforgettable experiences.
arXiv:2412.18038v1 Announce Type: new Abstract: Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. Deep generative models achieve top performance in this task, assuming enough labelled trajectories are available for training. To this end, large amounts of synthetically generated, labelled trajectories exist (e.g., generated by video games). However, such trajectories are not meant to represent pedestrian motion realistically and are ineffective at training a predictive model. We propose a method and an architecture to augment synthetic trajectories at training time and with an adversarial approach. We show that trajectory augmentation at training time unleashes significant gains when a state-of-the-art generative model is evaluated over real-world trajectories.
The article “Accurately predicting pedestrian trajectories: a novel approach using synthetic trajectory augmentation” explores the importance of accurately predicting pedestrian trajectories in various applications such as autonomous driving and service robotics. It highlights the success of deep generative models in this task but acknowledges the need for a large number of labeled trajectories for training. While synthetic trajectories generated by video games exist in abundance, they do not accurately represent real pedestrian motion and are ineffective for training predictive models. In response to this, the article proposes a method and architecture for augmenting synthetic trajectories at training time using an adversarial approach. The results demonstrate significant improvements in the performance of a state-of-the-art generative model when evaluated with real-world trajectories, highlighting the effectiveness of trajectory augmentation during training.
The Importance of Accurately Predicting Pedestrian Trajectories
Accurately predicting pedestrian trajectories plays a crucial role in various applications, including autonomous driving and service robotics. Being able to anticipate how pedestrians will move allows these systems to make informed decisions and take appropriate actions to ensure safety and efficiency. Deep generative models have emerged as the leading approach for this task, achieving top performance in trajectory prediction. However, these models heavily rely on the availability of labeled trajectories for training.
The Challenge of Synthetic Trajectories
In recent years, there has been a surge in the availability of labeled trajectories generated by video games and simulation environments. While these synthetic trajectories offer a large amount of labeled data, they do not accurately represent real-world pedestrian motion. As a result, using these trajectories alone to train predictive models can lead to ineffective performance in real-world scenarios.
A Solution: Trajectory Augmentation
To overcome the limitation of synthetic trajectories, we propose a novel method and architecture that augment these trajectories at training time using an adversarial approach. By augmenting the synthetic trajectories with realistic variations, we aim to bridge the gap between synthetic and real-world pedestrian motion. This approach not only improves the performance of generative models on real-world trajectories but also reduces the reliance on large amounts of manually labeled real-world data.
Unleashing Significant Gains
Our experiments have shown that trajectory augmentation at training time can unleash significant gains when evaluating a state-of-the-art generative model over real-world trajectories. By incorporating the augmented synthetic trajectories, the model exhibits improved accuracy and robustness in predicting the behavior of pedestrians in real-world scenarios.
The Architecture: Adversarial Trajectory Augmentation
The proposed architecture consists of two main components: a generator and a discriminator. The generator takes synthetic trajectories as input and transforms them to incorporate realistic variations. These variations can include changes in speed, direction, and other motion patterns that are prevalent in real-world pedestrian motion. The discriminator then evaluates the augmented trajectories to provide feedback to the generator, ensuring that the variations are realistic and plausible.
By iteratively training the generator and discriminator, the system learns to generate augmented trajectories that closely resemble real-world pedestrian motion. This adversarial approach allows the generative model to capture the nuances and complexities of real-world pedestrian behavior, leading to improved prediction accuracy.
The Road Ahead: Realistic Trajectory Generation
The proposed trajectory augmentation method and architecture represent a significant step towards enabling generative models to accurately predict pedestrian trajectories in real-world scenarios. Further research can explore enhancements and extensions to this approach, such as incorporating additional contextual information (e.g., scene semantics, pedestrian intentions) and refining the adversarial training process.
As more advanced deep generative models and trajectory augmentation techniques are developed, the potential applications expand beyond autonomous driving and service robotics. These models can find applications in crowd management, urban planning, and many other domains where accurately predicting pedestrian behavior is critical.
Key Takeaways:
Accurately predicting pedestrian trajectories is crucial for autonomous driving and service robotics.
Synthetic trajectories generated by video games are ineffective in training predictive models due to their lack of realism.
We propose a method and architecture for augmenting synthetic trajectories with realistic variations.
Trajectory augmentation at training time significantly improves the performance of generative models on real-world trajectories.
The proposed adversarial approach bridges the gap between synthetic and real-world pedestrian motion.
Further research can explore enhancements and applications of this trajectory augmentation method.
The paper “Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics” highlights the importance of accurately predicting pedestrian motion in various domains. The authors acknowledge the success of deep generative models in this task, but note that these models heavily rely on having a sufficient number of labeled trajectories for training.
One of the challenges in obtaining labeled pedestrian trajectories is the lack of realistic representations in existing synthetic datasets, such as those generated by video games. While these datasets offer a large number of labeled trajectories, they do not accurately capture the complexities and nuances of real-world pedestrian motion. As a result, using these synthetic trajectories alone for training a predictive model can be ineffective.
To address this limitation, the authors propose a method and architecture for augmenting synthetic trajectories during the training process using an adversarial approach. By augmenting the synthetic trajectories with real-world data, they aim to bridge the gap between synthetic and real pedestrian motion, and improve the performance of generative models when evaluated on real-world trajectories.
The authors demonstrate the effectiveness of their approach by evaluating a state-of-the-art generative model on real-world trajectories. The results show significant gains in accuracy and performance when the model is trained with augmented trajectories compared to using only synthetic trajectories. This highlights the potential of trajectory augmentation at training time to enhance the capabilities of generative models in predicting pedestrian motion.
Building on this work, future research could explore different methods of trajectory augmentation and investigate the impact of different real-world datasets on the performance of generative models. Additionally, it would be interesting to analyze the generalizability of the proposed approach across different domains and applications beyond autonomous driving and service robotics. Overall, this paper provides valuable insights and a promising direction for improving the accuracy of pedestrian trajectory prediction in real-world scenarios. Read the original article
