WideRange4D：實現高品質四維重建，適用於大範圍運動與場景

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

隨著三維重建技術的快速發展，四維重建的研究也在不斷推進，現有的四維重建方法已能生成高質量的四維場景。然而，由於獲取多視角視頻數據的挑戰，當前的四維重建基準主要展示的是在固定位置進行的動作，如舞蹈，且場景有限。在實際應用中，許多場景涉及大範圍的空間移動，這凸顯了現有四維重建數據集的局限性。此外，現有的四維重建方法依賴於形變場來估計三維物體的動態，但形變場難以處理大範圍的空間移動，這限制了實現高質量大範圍空間移動四維場景重建的能力。本文聚焦於具有顯著物體空間移動的四維場景重建，提出了一個新的四維重建基準——WideRange4D。該基準包含豐富的具有大空間變化的四維場景數據，能夠更全面地評估四維生成方法的生成能力。進一步地，我們提出了一種新的四維重建方法——Progress4D，該方法在各種複雜的四維場景重建任務中都能生成穩定且高質量的四維結果。我們在WideRange4D上進行了定量和定性的對比實驗，結果表明我們的Progress4D優於現有的最先進四維重建方法。項目地址：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