WideRange4D：实现大范围运动与场景的高质量四维重建

WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes

March 17, 2025

作者: Ling Yang, Kaixin Zhu, Juanxi Tian, Bohan Zeng, Mingbao Lin, Hongjuan Pei, Wentao Zhang, Shuicheng Yan

cs.AI

摘要

随着三维重建技术的快速发展，四维重建研究也在不断推进，现有的四维重建方法已能生成高质量的四维场景。然而，由于多视角视频数据获取的挑战，当前的四维重建基准主要局限于展示原地动作，如舞蹈等，场景范围有限。实际应用中，许多场景涉及大范围的空间运动，这凸显出现有四维重建数据集的局限性。此外，现有四维重建方法依赖变形场来估计三维物体的动态变化，但变形场难以处理大范围的空间运动，这限制了实现高质量大范围空间运动四维场景重建的能力。本文聚焦于具有显著物体空间运动的四维场景重建，提出了一个新颖的四维重建基准——WideRange4D。该基准包含丰富的大空间变化四维场景数据，能够更全面地评估四维生成方法的生成能力。进一步地，我们提出了一种新的四维重建方法——Progress4D，它在多种复杂四维场景重建任务中均能生成稳定且高质量的四维结果。我们在WideRange4D上进行了定量与定性的对比实验，结果表明Progress4D优于现有的顶尖四维重建方法。项目地址：https://github.com/Gen-Verse/WideRange4D。

English

With the rapid development of 3D reconstruction technology, research in 4D reconstruction is also advancing, existing 4D reconstruction methods can generate high-quality 4D scenes. However, due to the challenges in acquiring multi-view video data, the current 4D reconstruction benchmarks mainly display actions performed in place, such as dancing, within limited scenarios. In practical scenarios, many scenes involve wide-range spatial movements, highlighting the limitations of existing 4D reconstruction datasets. Additionally, existing 4D reconstruction methods rely on deformation fields to estimate the dynamics of 3D objects, but deformation fields struggle with wide-range spatial movements, which limits the ability to achieve high-quality 4D scene reconstruction with wide-range spatial movements. In this paper, we focus on 4D scene reconstruction with significant object spatial movements and propose a novel 4D reconstruction benchmark, WideRange4D. This benchmark includes rich 4D scene data with large spatial variations, allowing for a more comprehensive evaluation of the generation capabilities of 4D generation methods. Furthermore, we introduce a new 4D reconstruction method, Progress4D, which generates stable and high-quality 4D results across various complex 4D scene reconstruction tasks. We conduct both quantitative and qualitative comparison experiments on WideRange4D, showing that our Progress4D outperforms existing state-of-the-art 4D reconstruction methods. Project: https://github.com/Gen-Verse/WideRange4D