| title | 4D Human-Scene Reconstruction from Low-Overlap Captures |
|---|---|
| authors | Minhyuk Hwang, Sangmin Kim, Seunguk Do, Daneul Kim, Jaesik Park |
| date | 2026-07-10 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.09125 |
| affiliation | Seoul National University |
| upvotes | 50 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Existing volumetric capture of dynamic human performance achieves high fidelity with dense camera arrays. However, in real-world scenarios, only a handful of low-overlap cameras are available, which degrades the output quality and leaves large areas unobserved. Recent 4D reconstruction methods have focused on low-overlap settings, yet they still produce noticeable artifacts in under-observed regions. Video diffusion models have emerged as another option, but they show geometrically inconsistent results for humans. To address these limitations, we propose StudioRecon, a pipeline that reconstructs 4D human scenes from sparse, low-overlap cameras by decoupling background and humans. We densify background supervision by synthesizing hundreds of camera-controlled novel views with a video diffusion model. We also robustly initialize deformable Gaussian humans with cross-view identity association and triangulated multi-view keypoint fitting. Finally, our recursive enhancement module with motion-adaptive consistency injection harmonizes the composed output, thereby further avoiding remaining artifacts. We achieve state-of-the-art novel view synthesis across four real-world datasets and demonstrate applications such as novel trajectory rendering and human replacement. |
cite
@misc{hwang2026d,
title={4D Human-Scene Reconstruction from Low-Overlap Captures},
author={Minhyuk Hwang and Sangmin Kim and Seunguk Do and Daneul Kim and Jaesik Park},
year={2026},
eprint={2607.09125},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.09125}
}