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arXiv · 2507.23782

MonoFusion: Sparse-View 4D Reconstruction via Monocular Fusion

Zihan Wang·Jeff Tan·Tarasha Khurana·Neehar Peri·Deva Ramanan
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Abstract

We address the problem of dynamic scene reconstruction from sparse-view videos. Prior work often requires dense multi-view captures with hundreds of calibrated cameras (e.g. Panoptic Studio). Such multi-view setups are prohibitively expensive to build and cannot capture diverse scenes in-the-wild. In contrast, we aim to reconstruct dynamic human behaviors, such as repairing a bike or dancing, from a small set of sparse-view cameras with complete scene coverage (e.g. four equidistant inward-facing static cameras). We find that dense multi-view reconstruction methods struggle to adapt to this sparse-view setup due to limited overlap between viewpoints. To address these limitations, we carefully align independent monocular reconstructions of each camera to produce time- and view-consistent dynamic scene reconstructions. Extensive experiments on PanopticStudio and Ego-Exo4D demonstrate that our method achieves higher quality reconstructions than prior art, particularly when rendering novel views. Code, data, and data-processing scripts are available on https://github.com/Z1hanW/MonoFusion.

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Zihan Wang, Jeff Tan, Tarasha Khurana, Neehar Peri, Deva Ramanan. 2025-07-31. MonoFusion: Sparse-View 4D Reconstruction via Monocular Fusion. https://arxiv.org/abs/2507.23782

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