Reconstructing metrically accurate humans and their surrounding scenes from a single image is crucial for virtual reality, robotics, and comprehensive 3D scene understanding. However, existing methods struggle with depth ambiguity, occlusions, and physically inconsistent contacts.
To address these challenges, we introduce PhySIC, a unified framework for physically plausible Human–Scene Interaction and Contact reconstruction. PhySIC recovers metrically consistent SMPL-X human meshes, dense scene surfaces, and vertex-level contact maps within a shared coordinate frame, all from a single RGB image. Starting from coarse monocular depth and parametric body estimates, PhySIC performs occlusion-aware inpainting, fuses visible depth with unscaled geometry for a robust initial metric scene scaffold, and synthesizes missing support surfaces like floors. A confidence-weighted optimization subsequently refines body pose, camera parameters, and global scale by jointly enforcing depth alignment, contact priors, interpenetration avoidance, and 2D reprojection consistency.
Explicit occlusion masking safeguards invisible body regions against implausible configurations.
PhySIC is highly efficient, requiring only 9 seconds for a joint human-scene optimization and less than 27 seconds for end-to-end reconstruction process. Moreover, the framework naturally handles multiple humans, enabling reconstruction of diverse human scene interactions. Empirically, PhySIC substantially outperforms single-image baselines, reducing mean per-vertex scene error from 641 mm to 227 mm, halving the pose-aligned mean per-joint position error (PA-MPJPE) to 42 mm, and improving contact F1-score from 0.09 to 0.51. Qualitative results demonstrate that PhySIC yields realistic foot-floor interactions, natural seating postures, and plausible reconstructions of heavily occluded furniture. By converting a single image into a physically plausible 3D human-scene pair, PhySIC advances accessible and scalable 3D scene understanding.
