Recently, we have experienced a surge of interest in immersive media due to advances in 3D reconstruction techniques. Video reconstruction and free-viewpoint rendering, in particular, have emerged as powerful technologies, enabling enhanced user engagement and the generation of realistic environments. These methods have applications in various fields such as virtual reality, telepresence, metaverse, and 3D animation production.
However, video reconstruction comes with its fair share of challenges. We experience this especially when dealing with monocular perspectives and complex human-environment interactions. If things were simple, there would be no more challenges, but in practice, interacting with virtual environments is quite unpredictable. Therefore, it is difficult to work on them.
In visual field synthesis, Neural Radiance Fields (NeRF) have played a key role, and significant progress has been made. NeRF was originally proposed for reconstructing static 3D scenes from multi-view images. However, its great success gained attention and since then improvements have been made to address the challenges of dynamic view synthesis. Researchers have proposed several approaches to incorporate dynamic elements such as deformation fields and spatiotemporal radiation fields. Additionally, there is a particular focus on dynamic neural human modeling, which leverages estimated human poses as prior information. Although these advances have shown promise, accurately reconstructing difficult monocular videos with fast and complex movements and interactions of people, objects and scenes remains a major challenge.
What if we want to take NeRF even further so that we can accurately reconstruct complex human-environment interactions? How can we leverage NeRF in environments with complex object motion? time to meet Hosnef.
human-object-scene neural radiation field (Hosnef) was introduced to overcome the limitations of NeRF. Hosnef It addresses challenges related to complex object motion in human-object interactions and dynamic interactions between humans and different objects at different times. By incorporating the object bones attached to the human skeleton hierarchy, Hosnef It enables accurate estimation of object deformation during human-object interaction. Additionally, two new learnable object state embeddings were introduced to handle the dynamic removal and addition of objects in the static background and person object models.
development of Hosnef It involved exploring and identifying effective training objectives and strategies. Key considerations include deformation cycle consistency, optical flow monitoring, and foreground and background rendering. Hosnef Enables high-fidelity dynamic new view composition. You can also pause the monocular video at any time and render all scene details including dynamic people, objects and backgrounds from any perspective.So you can literally enjoy the infamous Neo dodging bullets scene of matrix movie.
Hosnef provides a breakthrough framework for 360° free-viewpoint high-fidelity novel view synthesis of dynamic scenes containing human-environment interactions, all from a single video. With the introduction of object bones and state condition expressions, Hosnef Effectively handles complex non-rigid body motions and interactions between humans, objects and the environment.
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Ekrem Cetinkaya graduated with a Bachelor of Science degree. He completed his master’s degree in 2018. He graduated in 2019 from Ožegin University, Turkiye, Istanbul. he wrote his master’s degree. A paper on image denoising using deep convolutional networks. He is currently pursuing his PhD. He completed his degree at Klagenfurt University in Austria and is working as a researcher on the ATHENA project. His research interests include deep learning, his vision of computers, and multimedia networking.
