Dynamic 2D Gaussians: Geometrically accurate radiance fields for dynamic objects
<|reference_start|>Dynamic 2D Gaussians: Geometrically accurate radiance fields for dynamic objects: Reconstructing objects and extracting high-quality surfaces play a vital role in the real world. Current 4D representations show the ability to render high-quality novel views for dynamic objects but cannot reconstruct high-quality meshes due to their implicit or geometrically inaccurate representations. In this paper, we propose a novel representation that can reconstruct accurate meshes from sparse image input, named Dynamic 2D Gaussians (D-2DGS). We adopt 2D Gaussians for basic geometry representation and use sparse-controlled points to capture 2D Gaussian's deformation. By extracting the object mask from the rendered high-quality image and masking the rendered depth map, a high-quality dynamic mesh sequence of the object can be extracted. Experiments demonstrate that our D-2DGS is outstanding in reconstructing high-quality meshes from sparse input. More demos and code are available at https://github.com/hustvl/Dynamic-2DGS.<|reference_end|>
@article{zhang2024dynamic,
    title={Dynamic 2D Gaussians: Geometrically accurate radiance fields for dynamic
  objects},
    author={Shuai Zhang, Guanjun Wu, Xinggang Wang, Bin Feng, Wenyu Liu},
    journal={arXiv preprint arXiv:2409.14072},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14072},
    primaryClass={cs.CV}
}