# General Dynamic Scene Reconstruction from Multiple View Video

arXiv: https://arxiv.org/abs/1509.09294 · HTML: https://mlsanity.com/paper/1509.09294/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2015.

## Built on by (most cited first, 19 of 19)

- [Modeling Clothing as a Separate Layer for an Animatable Human Avatar](https://mlsanity.com/paper/2106.14879/) (2021)
- [Vivid-ZOO: Multi-View Video Generation with Diffusion Model](https://mlsanity.com/paper/2406.08659/) (2024)
- [Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction](https://mlsanity.com/paper/2103.16341/) (2021)
- [Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras](https://mlsanity.com/paper/2110.10355/) (2021)
- [AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture](https://mlsanity.com/paper/2207.02031/) (2022)
- [Learning Compositional Representation for 4D Captures with Neural ODE](https://mlsanity.com/paper/2103.08271/) (2021)
- [POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture](https://mlsanity.com/paper/2103.15331/) (2021)
- [HiFECap: Monocular High-Fidelity and Expressive Capture of Human Performances](https://mlsanity.com/paper/2210.05665/) (2022)
- [Dynamic Scene Reconstruction: Recent Advance in Real-time Rendering and Streaming](https://mlsanity.com/paper/2503.08166/) (2025)
- [RFNet-4D++: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds with Cross-Attention Spatio-Temporal Features](https://mlsanity.com/paper/2203.16482/) (2022)
- [View Birdification in the Crowd: Ground-Plane Localization from Perceived Movements](https://mlsanity.com/paper/2111.05060/) (2021)
- [Geometry-aware Two-scale PIFu Representation for Human Reconstruction](https://mlsanity.com/paper/2112.02082/) (2021)
- [Temporal Consistency Loss for High Resolution Textured and Clothed 3DHuman Reconstruction from Monocular Video](https://mlsanity.com/paper/2104.09259/) (2021)
- [A Kendall Shape Space Approach to 3D Shape Estimation from 2D Landmarks](https://mlsanity.com/paper/2207.12687/) (2022)
- [Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras with Human Semantics](https://mlsanity.com/paper/2412.18785/) (2024)
- [CrossHuman: Learning Cross-Guidance from Multi-Frame Images for Human Reconstruction](https://mlsanity.com/paper/2207.09735/) (2022)
- [A Deeper Look into DeepCap](https://mlsanity.com/paper/2111.10563/) (2021)
- [Advances in 4D Generation: A Survey](https://mlsanity.com/paper/2503.14501/) (2025)
- [Reliable Multi-view 3D Reconstruction for `Just-in-time' Edge Environments](https://mlsanity.com/paper/2508.15158/) (2025)

## Abstract (arXiv)

This paper introduces a general approach to dynamic scene reconstruction from multiple moving cameras without prior knowledge or limiting constraints on the scene structure, appearance, or illumination. Existing techniques for dynamic scene reconstruction from multiple wide-baseline camera views primarily focus on accurate reconstruction in controlled environments, where the cameras are fixed and calibrated and background is known. These approaches are not robust for general dynamic scenes captured with sparse moving cameras. Previous approaches for outdoor dynamic scene reconstruction assume prior knowledge of the static background appearance and structure. The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras. Evaluation is performed on a variety of indoor and outdoor scenes with cluttered backgrounds and multiple dynamic non-rigid objects such as people. Comparison with state-of-the-art approaches demonstrates improved accuracy in both multiple view segmentation and dense reconstruction. The proposed approach also eliminates the requirement for prior knowledge of scene structure and appearance.