StudioRecon: Low-Overlap 4D Human Reconstruction
SeoulNatlUniv · hf · 2026-07-14
The authors propose StudioRecon for reconstructing 4D human scenes from a sparse set of low-overlap cameras. The problem arises because real-world scenes often lack dense camera arrays, degrading traditional volumetric capture performance. Existing low-overlap 4D reconstruction methods still show noticeable artifacts in under-observed areas, and video diffusion models often suffer from geometric inconsistencies.
Method
- Separates background and human processing.
- Uses a video diffusion model to synthesize hundreds of camera-controlled novel views to enhance background supervision.
- Fits a robust initialization for deformable Gaussian humans using cross-view identity association and triangulated keypoints.
- Further fuses outputs and reduces residual artifacts via a recursive enhancement module and motion-adaptive consistency injection.
Results & Applications
The authors claim the method achieves state-of-the-art novel view synthesis performance on 4 real-world datasets and demonstrates:
- Novel trajectory rendering
- Human replacement
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