C H R O M M
Coherent Human-Scene Reconstruction from
Multi-Person Multi-View Video in a Single Pass

Sangmin Kim1,2, Minhyuk Hwang1, Geonho Cha2, Dongyoon Wee2, Jaesik Park1
1 Seoul National University     2 NAVER Cloud
arXiv Paper Code

TL;DR: Given multi-person, multi-view videos, CHROMM reconstructs cameras, scene point cloud, and human meshes in a single pass.

Abstract

Recent advances in 3D foundation models have led to growing interest in reconstructing humans and their surrounding environments. However, most existing approaches focus on monocular inputs, and extending them to multi-view settings requires additional overhead modules or preprocessed data. To this end, we present CHROMM, a unified framework that jointly estimates cameras, scene point clouds, and human meshes from multi-person multi-view videos without relying on external modules or preprocessing. We integrate strong geometric and human priors from Pi3X and Multi-HMR into a single trainable neural network architecture, and introduce a scale adjustment module to solve the scale discrepancy between humans and the scene. We also introduce a multi-view fusion strategy to aggregate per-view estimates into a single representation at test-time. Finally, we propose a geometry-based multi-person association method, which is more robust than appearance-based approaches. Experiments on EMDB, RICH, EgoHumans, and EgoExo4D show that CHROMM achieves competitive performance in global human motion and multi-view pose estimation while running over 8x faster than prior optimization-based multi-view approaches.

Method

Pipeline image.
Overview of our pipeline. Each frame is encoded by the Pi3 encoder and the Multi-HMR encoder. The Pi3 features are decoded to reconstruct the scene. Head tokens detected from Multi-HMR features are fused with the corresponding tokens from the Pi3 decoder tokens to predict SMPL parameters. At test time, we associate persons across views and fuse them into a global representation, followed by a scale adjustment module to align humans and the scene.

Experiments

Pipeline image.
Experiments on EMDB, RICH, EgoHumans, and EgoExo4D show that CHROMM achieves competitive or superior performance in global human motion and multi-view pose estimation tasks. In particular, Table 1 shows that our model achieves strong results in both monocular and multi-view settings.

Interactive Viewer

Explore the reconstructed humans and the scene interactively.

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Multi-View Results

Given multi-view video, CHROMM reconstructs coherent human meshes and the surrounding scene in diverse settings.

Monocular Results

CHROMM can also be applied to a monocular set up.

BibTeX

@article{kim2026chromm,
  author    = {Kim, Sangmin and Hwang, Minhyuk and Cha, Geonho and Wee, Dongyoon and Park, Jaesik},
  title     = {Coherent Human-Scene Reconstruction from Multi-Person Multi-View Video in a Single Pass},
  journal   = {arXiv},
  year      = {2026}
}