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 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
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Ling Yang
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2025-03-18T09:22:51.251Z
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Ling Yang
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Kaixin Zhu
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2025-03-18T09:16:44.192Z
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kaixin zhu
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Juanxi Tian
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2025-03-18T08:08:12.128Z
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Juanxi Tian
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Juanxi
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Bohan Zeng
claimed_verified
2025-03-18T08:08:15.654Z
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bohan zeng
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Mingbao Lin
admin_assigned
2025-03-18T09:22:58.991Z
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Mingbao Lin
user
mingbao
67d8dd0b924be985c277c8fb
Hongjuan Pei
67d8dd0b924be985c277c8fc
Wentao Zhang
67d8dd0b924be985c277c8fd
Shuicheng Yan
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 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
A novel 4D reconstruction benchmark, WideRange4D, and method, Progress4D, are introduced to address challenges in reconstructing scenes with wide-range object spatial movements.
4D reconstruction 4D scene data large spatial variations deformation fields wide-range spatial movements Progress4D WideRange4D 4D generation methods