ShowMak3r: Compositional TV Show Reconstruction

Sangmin Kim, Seunguk Do, Jaesik Park
4/4/2026

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Abstract

Reconstructing dynamic radiance fields from video clips is challenging, especially when entertainment videos like TV shows are given. Many challenges make the reconstruction difficult due to (1) actors occluding with each other and having diverse facial expressions, (2) cluttered stages, and (3) small baseline views or sudden shot changes. To address these issues, we present ShowMak3r, a comprehensive reconstruction pipeline that allows the editing of scenes like how video clips are made in a production control room. In ShowMak3r, a 3DLocator module locates recovered actors on the stage using depth prior and estimates unseen human poses via interpolation. The proposed ShotMatcher module then tracks the actors under shot changes. Furthermore, ShowMak3r introduces a face-fitting network that dynamically recovers the actors’ expressions. Experiments on Sitcoms3D dataset show that our pipeline can reassemble TV show scenes with new cameras at different timestamps. We also demonstrate that ShowMak3r enables interesting applications such as synthetic shot-making, actor relocation, insertion, deletion, and pose manipulation. Project page : https://nstar1125.github.io/showmak3r

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Cite this paper

@article{kim2026showmakr,
  title  = {ShowMak3r: Compositional TV Show Reconstruction},
  author = {Sangmin Kim and Seunguk Do and Jaesik Park},
  year   = {2026},
  doi    = {10.1109/CVPR52734.2025.00089},
  url    = {https://doi.org/10.1109/CVPR52734.2025.00089},
  journal = {Computer Vision and Pattern Recognition}
}

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