Reconstructing 3D Human-Object Interaction via a Unified Triplane Space

Reconstructing 3D Human-Object Interaction (HOI) from asingle image is challenging due to the diversity of the reconstructed tar-gets. Ordinarily, the human body follows a fixed parametric template,whereas objects exhibit diverse and irregular shapes. Such differenceslead to inconsistent geometric priors and scale variations, making jointmodeling within a unified framework highly difficult. Existing vertex-level methods struggle to effectively handle this structural discrepancy.We propose to bridge this gap by introducing a unified triplane featuremap that models both human and object meshes within a shared 3Dlatent space. Our framework comprises three components: (1) a HOI Tri-plane Variational AutoEncoder (HOIT-VAE) that learns a compact andstable triplane latent space from large-scale human-object mesh data; (2)a HOI Triplane Transformer (HOIT-T) that predicts triplane featuresfrom a single image, regularized by the learned priors of HOIT-VAE;(3) a pose-fitting method for estimating the human pose from the re-constructed human mesh. Experiments on the BEHAVE and InterCapdatasets show that our approach achieves competitive reconstruction per-formance, validating the effectiveness of the unified triplane representa-tion in capturing the mesh structures and interactions between humansand objects.