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Reconstructing human-object interaction in 3D from a single RGB image is achallenging task and existing data driven methods do not generalize beyond theobjects present in the carefully curated 3D interaction datasets. Capturinglarge-scale real data to learn strong interaction and 3D shape priors is veryexpensive due to the combinatorial nature of human-object interactions. In thispaper, we propose ProciGen (Procedural interaction Generation), a method toprocedurally generate datasets with both, plausible interaction and diverseobject variation. We generate 1M+ human-object interaction pairs in 3D andleverage this large-scale data to train our HDM (Hierarchical Diffusion Model),a novel method to reconstruct interacting human and unseen objects, without anytemplates. Our HDM is an image-conditioned diffusion model that learns bothrealistic interaction and highly accurate human and object shapes. Experimentsshow that our HDM trained with ProciGen significantly outperforms prior methodsthat requires template meshes and that our dataset allows training methods withstrong generalization ability to unseen object instances. Our code and datawill be publicly released at:https://virtualhumans.mpi-inf.mpg.de/procigen-hdm.

Template Free Reconstruction of Human-object Interaction with Procedural Interaction Generation