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Researchers from the Korea Advanced Institute of Science and Technology (KAIST) and Google DeepMind have proposed a 4D novel view synthesis model, FL4N, which can reconstruct dynamic scenes from monocular video and render views at different viewpoints and time points in real time. The related paper has been published on arXiv, and the code and model weights have been released as open source simultaneously. FL4N adopts feed-forward latent space modeling, introducing a lightweight dynamic encoder based on a pretrained static scene model, and learns object motion through temporal residual learning, reducing reliance on complex 3D motion annotations. On the NVIDIA Dynamic Scenes, N3DV, and Kubric4D datasets, it outperforms the compared 4D Gaussian methods on multiple image quality metrics, and is expected to be used in XR content production, immersive video, and dynamic digital twins.
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KAIST and Google DeepMind propose 4D novel view synthesis model FL4N
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Oct 28
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KAIST and Google DeepMind open-source FL4N: real-time reconstruction of 4D dynamic scenes from monocular video
The Korea Advanced Institute of Science and Technology (KAIST) and Google DeepMind jointly proposed FL4N, a 4D novel view synthesis model that can reconstruct dynamic scenes from monocular video and render images from different viewpoints and time points in real time. The paper has been published on arXiv, and the code and model weights have been open-sourced simultaneously. FL4N adopts feed-forward latent space modeling, introducing a lightweight dynamic encoder based on a pretrained static scene model, and learns object motion through temporal residuals, reducing reliance on complex 3D motion annotations. On the NVIDIA Dynamic Scenes, N3DV, and Kubric4D datasets, it outperforms the compared 4D Gaussian methods on multiple image quality metrics, and is expected to be used in XR content production, immersive video, and dynamic digital twins.