Coherent Human-Scene Reconstruction from Multi-Person Multi-View Video in a Single Pass
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. Project page: https://nstar1125.github.io/chromm.
69badba6ba2b62a5fb0b0b0c
Sangmin Kim
claimed_verified
2026-03-19T12:58:06.763Z
66270c20846c2a66a7a7c299
/avatars/0cddccc9584b1062faaff164f0de6e22.svg
Sangmin
nstar1125
user
nstar1125
69badba6ba2b62a5fb0b0b0d
Minhyuk Hwang
69badba6ba2b62a5fb0b0b0e
Geonho Cha
69badba6ba2b62a5fb0b0b0f
Dongyoon Wee
69badba6ba2b62a5fb0b0b10
Jaesik Park
Coherent Human-Scene Reconstruction from Multi-Person Multi-View Video in a Single Pass
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. Project page: https://nstar1125.github.io/chromm.
CHROMM is a unified framework that jointly estimates cameras, scene point clouds, and human meshes from multi-person multi-view videos using integrated geometric and human priors with improved speed and robustness.
3D foundation models multi-person multi-view videos camera estimation scene point clouds human meshes Pi3X Multi-HMR neural network architecture scale adjustment module multi-view fusion strategy geometry-based multi-person association