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Reconstructing the human body from monocular video input presents significant challenges, including a limited field of view and difficulty in capturing non-rigid deformations, such as those associated with clothing and pose variations. These challenges often compromise motion editability and rendering quality. To address these issues, we propose a cloth-aware 3D Gaussian splatting approach that leverages the strengths of 2D convolutional neural networks (CNNs) and 3D Gaussian splatting for high-quality human body reconstruction from monocular video. Our method parameterizes 3D Gaussians anchored to a human template to generate posed position maps that capture pose-dependent non-rigid deformations. Additionally, we introduce Learnable Cloth Features, which are pixel-aligned with the posed position maps to address cloth-related deformations. By jointly modeling cloth and pose-dependent deformations, along with compact, optimizable linear blend skinning (LBS) weights, our approach significantly enhances the quality of monocular 3D human reconstructions. We also incorporate carefully designed regularization techniques for the Gaussians, improving the generalization capability of our model. Experimental results demonstrate that our method outperforms state-of-the-art techniques for animatable avatar reconstruction from monocular inputs, delivering superior performance in both reconstruction fidelity and rendering quality.
The Charta of Rigas Velestinlis is one of the most important works of the eighteenth-century Neo-Hellenic Enlightenment and the most characteristic sample of Greek scholar cartography. Printed in Vienna in 1220 copies in 1796-1797, this emblematic map of the Balkan peninsula significantly influenced the development of ideas and perspectives that inspired the Greek War of Independence from the Ottoman Empire in 1821. The sixty (60) known remaining copies of this valuable material in Greece and abroad remain stored in the confined spaces of libraries, museums, and archives, strictly guarded for security, conservation, and preservation. This renders their access difficult to both the general public and the educational community. Since the Onassis Library and the General State Archives of Greece—Cartographic Heritage Archives both possess an original copy of this historical document each, they design and implement many educational programs aiming at highlighting its importance and reintroducing it to the public. This paper will present how the usage of new technologies, both in software and hardware, has facilitated the showcasing of cultural heritage artifacts, such as Rigas’ Charta, with an emphasis on technologies and resources that are freely available to everyone. It will also be demonstrated how the digitization projects, the digital libraries, repositories, and platforms implemented by many cultural and research organizations during the last decade, presented the opportunity for the new generation to come in contact with a variety of “locked away” historical documents, like Rigas’ Charta, allowing their reuse and reinterpretation while providing unlimited potential for the collection, research and presentation of facts, evidence and data. Furthermore, the incorporation of this digital cultural wealth in the school curriculum through targeted educational programs and the creative combination with open-source metaverse development tools, unleashed the possibilities of reviving the past, extending the life span of old materials to perpetuity. As a result, this multimodal approach paved the way for the emergence of a new more democratic, open-access, and inclusive educational model.
In this groundbreaking study, we present a novel approach to interspecies communication, focusing on the understanding of chicken vocalizations. Leveraging advanced mathematical models in artificial intelligence (AI) and machine learning, we have developed a system capable of interpreting various emotional states in chickens, including hunger, fear, anger, contentment, excitement, and distress. Our methodology employs a cutting-edge AI technique we call Deep Emotional Analysis Learning (DEAL), a highly mathematical and innovative approach that allows for the nuanced understanding of emotional states through auditory data. DEAL is rooted in complex mathematical algorithms, enabling the system to learn and adapt to new vocal patterns over time. We conducted our study with a sample of 80 chickens, meticulously recording and analyzing their vocalizations under various conditions. To ensure the accuracy of our system’s interpretations, we collaborated with a team of eight animal psychologists and veterinary surgeons, who provided expert insights into the emotional states of the chickens. Our system demonstrated an impressive accuracy rate of close to 80%, marking a significant advancement in the field of animal communication. This research not only opens up new avenues for understanding and improving animal welfare but also sets a precedent for further studies in AI-driven interspecies communication. The novelty of our approach lies in its application of sophisticated AI techniques to a largely unexplored area of study. By bridging the gap between human and animal communication, we believe our research will pave the way for more empathetic and effective interactions with the animal kingdom.
Garment-aware gaussian for clothed human modeling from monocular video
Article ID: 3146
Vol 6, Issue 2, 2025
DOI: https://doi.org/10.54517/m3146
Vol 6, Issue 2, 2025
Received: 9 December 2024; Accepted: 14 March 2025; Available online: 27 March 2025; Issue release: 30 June 2025
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Abstract
Reconstructing the human body from monocular video input presents significant challenges, including a limited field of view and difficulty in capturing non-rigid deformations, such as those associated with clothing and pose variations. These challenges often compromise motion editability and rendering quality. To address these issues, we propose a cloth-aware 3D Gaussian splatting approach that leverages the strengths of 2D convolutional neural networks (CNNs) and 3D Gaussian splatting for high-quality human body reconstruction from monocular video. Our method parameterizes 3D Gaussians anchored to a human template to generate posed position maps that capture pose-dependent non-rigid deformations. Additionally, we introduce Learnable Cloth Features, which are pixel-aligned with the posed position maps to address cloth-related deformations. By jointly modeling cloth and pose-dependent deformations, along with compact, optimizable linear blend skinning (LBS) weights, our approach significantly enhances the quality of monocular 3D human reconstructions. We also incorporate carefully designed regularization techniques for the Gaussians, improving the generalization capability of our model. Experimental results demonstrate that our method outperforms state-of-the-art techniques for animatable avatar reconstruction from monocular inputs, delivering superior performance in both reconstruction fidelity and rendering quality.
Keywords
neural rendering; 3D reconstructing; 3D Gaussian splatting; clothing human modeling; animatable body
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