FLAG-4D: Flow-Guided Local-Global Dual-Deformation Model for 4D Reconstruction

• URL: http://arxiv.org/abs/2602.08558v1
• Date: Mon, 09 Feb 2026 11:55:15 GMT
• Title: FLAG-4D: Flow-Guided Local-Global Dual-Deformation Model for 4D Reconstruction
• Authors: Guan Yuan Tan, Ngoc Tuan Vu, Arghya Pal, Sailaja Rajanala, Raphael Phan C. -W., Mettu Srinivas, Chee-Ming Ting,
• Abstract summary: FLAG-4D reconstructs how 3D Gaussian primitives evolve through space and time.<n>It achieves higher-fidelity and more temporally coherent reconstructions with finer detail than state-of-the-art methods.
• Score: 7.144085821875197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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