---
title: "WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes"
canonical_url: "https://www.modelscope.cn/papers/127577"
md_url: "https://www.modelscope.cn/papers/127577.md"
arxiv_id: 2503.13435
published: 2025-04-29
last_updated: 2025-04-29
authors:
  - "Ling Yang"
  - "Kaixin Zhu"
  - "Juanxi Tian"
  - "Bohan Zeng"
  - "Mingbao Lin"
  - "Hongjuan Pei"
  - "Wentao Zhang"
  - "Shuicheng Yan"
model_name: Progress4D
model_developer: "北京大学、Skywork AI、中国科学院大学、新加坡国立大学"
domain:
  - "计算机视觉"
  - "4D重建"
  - "3D高斯泼溅"
  - "动态场景生成"
  - "基准测试"
type:
  - "计算机视觉"
  - "4D重建"
  - "3D高斯泼溅"
  - "动态场景生成"
  - "基准测试"
  - "Computer Vision and Pattern Recognition"
arxiv_url: "https://arxiv.org/abs/2503.13435"
pdf_url: "https://arxiv.org/pdf/2503.13435"
code_link: "https://github.com/Gen-Verse/WideRange4D"
---

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

> 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…

「WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes」是 ModelScope 魔搭社区收录的论文，arXiv 2503.13435，作者为 Ling Yang, Kaixin Zhu, Juanxi Tian et al.，发表于 2025-04-29，属于 计算机视觉、4D重建、3D高斯泼溅 领域。

- **ArXiv**: 2503.13435
- **Published**: 2025-04-29
- **Authors**: Ling Yang, Kaixin Zhu, Juanxi Tian, Bohan Zeng, Mingbao Lin, Hongjuan Pei, Wentao Zhang, Shuicheng Yan
- **Model**: Progress4D
- **Developer**: 北京大学、Skywork AI、中国科学院大学、新加坡国立大学
- **Domain**: 计算机视觉, 4D重建, 3D高斯泼溅, 动态场景生成, 基准测试
- **ArXiv URL**: https://arxiv.org/abs/2503.13435
- **PDF**: https://arxiv.org/pdf/2503.13435
- **Code**: https://github.com/Gen-Verse/WideRange4D

Source: https://www.modelscope.cn/papers/127577

---

> WideRange4D：实现大范围运动与场景的高质量4D重建

## 摘要

本文提出了WideRange4D基准和Progress4D方法，旨在解决现有4D重建技术在处理大范围空间运动时的局限性。WideRange4D是一个包含丰富真实与虚拟场景的4D重建基准，涵盖多种天气、环境和运动复杂度。Progress4D是一种两阶段4D重建框架，首先进行高质量3D场景初始化，随后通过渐进式时间步对齐策略拟合4D动态，结合自适应权重的时间步对齐损失函数，有效解决了大范围运动中前景物体模糊或消失的问题，在WideRange4D基准上取得了最优性能。

## 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