  Human-object interactions (HOIs) are crucial for human-centric scene
understanding applications such as human-centric visual generation, AR/VR, and
robotics. Since existing methods mainly explore capturing HOIs, rendering HOI
remains less investigated. In this paper, we address this challenge in HOI
animation from a compositional perspective, i.e., animating novel HOIs
including novel interaction, novel human and/or novel object driven by a novel
pose sequence. Specifically, we adopt neural human-object deformation to model
and render HOI dynamics based on implicit neural representations. To enable the
interaction pose transferring among different persons and objects, we then
devise a new compositional conditional neural radiance field (or CC-NeRF),
which decomposes the interdependence between human and object using latent
codes to enable compositionally animation control of novel HOIs. Experiments
show that the proposed method can generalize well to various novel HOI
animation settings. Our project page is https://zhihou7.github.io/CHONA/
