FLAG-4D: Flow-Guided Local-Global Dual-Deformation Model for 4D Reconstruction
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914315645222912 |
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| author | Tan, Guan Yuan Vu, Ngoc Tuan Pal, Arghya Rajanala, Sailaja -W., Raphael Phan C. Srinivas, Mettu Ting, Chee-Ming |
| author_facet | Tan, Guan Yuan Vu, Ngoc Tuan Pal, Arghya Rajanala, Sailaja -W., Raphael Phan C. Srinivas, Mettu Ting, Chee-Ming |
| contents | We introduce FLAG-4D, a novel framework for generating novel views of dynamic scenes by reconstructing how 3D Gaussian primitives evolve through space and time. Existing methods typically rely on a single Multilayer Perceptron (MLP) to model temporal deformations, and they often struggle to capture complex point motions and fine-grained dynamic details consistently over time, especially from sparse input views. Our approach, FLAG-4D, overcomes this by employing a dual-deformation network that dynamically warps a canonical set of 3D Gaussians over time into new positions and anisotropic shapes. This dual-deformation network consists of an Instantaneous Deformation Network (IDN) for modeling fine-grained, local deformations and a Global Motion Network (GMN) for capturing long-range dynamics, refined through mutual learning. To ensure these deformations are both accurate and temporally smooth, FLAG-4D incorporates dense motion features from a pretrained optical flow backbone. We fuse these motion cues from adjacent timeframes and use a deformation-guided attention mechanism to align this flow information with the current state of each evolving 3D Gaussian. Extensive experiments demonstrate that FLAG-4D achieves higher-fidelity and more temporally coherent reconstructions with finer detail preservation than state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08558 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FLAG-4D: Flow-Guided Local-Global Dual-Deformation Model for 4D Reconstruction Tan, Guan Yuan Vu, Ngoc Tuan Pal, Arghya Rajanala, Sailaja -W., Raphael Phan C. Srinivas, Mettu Ting, Chee-Ming Computer Vision and Pattern Recognition Computer Science and Game Theory We introduce FLAG-4D, a novel framework for generating novel views of dynamic scenes by reconstructing how 3D Gaussian primitives evolve through space and time. Existing methods typically rely on a single Multilayer Perceptron (MLP) to model temporal deformations, and they often struggle to capture complex point motions and fine-grained dynamic details consistently over time, especially from sparse input views. Our approach, FLAG-4D, overcomes this by employing a dual-deformation network that dynamically warps a canonical set of 3D Gaussians over time into new positions and anisotropic shapes. This dual-deformation network consists of an Instantaneous Deformation Network (IDN) for modeling fine-grained, local deformations and a Global Motion Network (GMN) for capturing long-range dynamics, refined through mutual learning. To ensure these deformations are both accurate and temporally smooth, FLAG-4D incorporates dense motion features from a pretrained optical flow backbone. We fuse these motion cues from adjacent timeframes and use a deformation-guided attention mechanism to align this flow information with the current state of each evolving 3D Gaussian. Extensive experiments demonstrate that FLAG-4D achieves higher-fidelity and more temporally coherent reconstructions with finer detail preservation than state-of-the-art methods. |
| title | FLAG-4D: Flow-Guided Local-Global Dual-Deformation Model for 4D Reconstruction |
| topic | Computer Vision and Pattern Recognition Computer Science and Game Theory |
| url | https://arxiv.org/abs/2602.08558 |