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

• URL: http://arxiv.org/abs/2503.13435v1
• Date: Mon, 17 Mar 2025 17:58:18 GMT
• Title: WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes
• Authors: Ling Yang, Kaixin Zhu, Juanxi Tian, Bohan Zeng, Mingbao Lin, Hongjuan Pei, Wentao Zhang, Shuicheng Yan,
• Abstract summary: We propose a novel 4D reconstruction benchmark, WideRange4D.<n>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.<n>We also introduce a new 4D reconstruction method, Progress4D, which generates stable and high-quality 4D results across various complex 4D scene reconstruction tasks.
• Score: 65.76371201992654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

Related papers

• Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction [72.54905331756076]
  We introduce Geo4D, a method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic prior captured by such video models, Geo4D can be trained using only synthetic data.
  arXiv  Detail & Related papers  (2025-04-10T17:59:55Z)
• Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene [122.42861221739123]
  This paper investigates a novel framework for 4D-PSG generation that leverages rich 2D visual scene annotations to enhance 4D scene learning. We propose a 2D-to-4D visual scene transfer learning framework, where a spatial-temporal scene strategy effectively transfers dimension-invariant features from abundant 2D SG annotations to 4D scenes.
  arXiv  Detail & Related papers  (2025-03-19T09:16:08Z)
• Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models [116.31344506738816]
  We present a novel framework, textbfDiffusion4D, for efficient and scalable 4D content generation. We develop a 4D-aware video diffusion model capable of synthesizing orbital views of dynamic 3D assets. Our method surpasses prior state-of-the-art techniques in terms of generation efficiency and 4D geometry consistency.
  arXiv  Detail & Related papers  (2024-05-26T17:47:34Z)
• Comp4D: LLM-Guided Compositional 4D Scene Generation [65.5810466788355]
  We present Comp4D, a novel framework for Compositional 4D Generation. Unlike conventional methods that generate a singular 4D representation of the entire scene, Comp4D innovatively constructs each 4D object within the scene separately. Our method employs a compositional score distillation technique guided by the pre-defined trajectories.
  arXiv  Detail & Related papers  (2024-03-25T17:55:52Z)
• 4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency [118.15258850780417]
  We present textbf4DGen, a novel framework for grounded 4D content creation.<n>Our pipeline facilitates controllable 4D generation, enabling users to specify the motion via monocular video or adopt image-to-video generations.<n>Compared to existing video-to-4D baselines, our approach yields superior results in faithfully reconstructing input signals.
  arXiv  Detail & Related papers  (2023-12-28T18:53:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.