Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

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

TL;DR

WideRange4D introduces a benchmark for 4D reconstruction under wide-range spatial movements and diverse scenes, addressing a key gap in existing datasets. It couples a two-stage Progress4D framework with high-quality 3D scene initialization and progressive 4D dynamic fitting to stabilize reconstruction when deformation fields alone fail. Quantitative and qualitative results on WideRange4D show Progress4D achieving state-of-the-art fidelity (lower L1 and higher PSNR/SSIM, lower LPIPS) while existing methods struggle with large-scale motions. The work offers a practical benchmark and a scalable method with strong implications for applications in gaming, visual effects, and dynamic scene understanding.

Abstract

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

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

TL;DR

WideRange4D introduces a benchmark for 4D reconstruction under wide-range spatial movements and diverse scenes, addressing a key gap in existing datasets. It couples a two-stage Progress4D framework with high-quality 3D scene initialization and progressive 4D dynamic fitting to stabilize reconstruction when deformation fields alone fail. Quantitative and qualitative results on WideRange4D show Progress4D achieving state-of-the-art fidelity (lower L1 and higher PSNR/SSIM, lower LPIPS) while existing methods struggle with large-scale motions. The work offers a practical benchmark and a scalable method with strong implications for applications in gaming, visual effects, and dynamic scene understanding.

Abstract

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

Paper Structure

This paper contains 29 sections, 5 equations, 11 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. 4D Dataset
  4. 4D Generation Methods
  5. New 4D Benchmark - WideRange4D
  6. Data Categories
  7. Statistical Analysis
  8. Data Acquisition and Annotation
  9. New Baseline Method
  10. Preliminaries
  11. The Progress4D Framework
  12. High-quality scene initialization.
  13. 4D dynamics progressive fitting.
  14. Experiment
  15. Implementation Details
  16. ...and 14 more sections

Figures (11)

  • Figure 1: Overview of our WideRange4D, which features wide-range spatial movements and a wide variety of scenes.
  • Figure 2: Visualization of the 4D scenes generated by our Progress4D and 4DGS wu20244d on the WideRange4D. Existing 4D reconstruction methods struggle to generate 4D scenes with wide-range movements, highlighting the need for our proposed new benchmark WideRange4D, and the high-quality 4D scenes produced by Progress4D validate the effectiveness of our method.
  • Figure 3: Exhibition of Testing Examples in WideRange4D.
  • Figure 4: Statistical Distribution of WideRange4D.
  • Figure 5: Exhibition of foreground objects
  • ...and 6 more figures