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Toward unlocking the potential of generative models in immersive 4Dexperiences, we introduce Virtual Pet, a novel pipeline to model realistic anddiverse motions for target animal species within a 3D environment. Tocircumvent the limited availability of 3D motion data aligned withenvironmental geometry, we leverage monocular internet videos and extractdeformable NeRF representations for the foreground and static NeRFrepresentations for the background. For this, we develop a reconstructionstrategy, encompassing species-level shared template learning and per-videofine-tuning. Utilizing the reconstructed data, we then train a conditional 3Dmotion model to learn the trajectory and articulation of foreground animals inthe context of 3D backgrounds. We showcase the efficacy of our pipeline withcomprehensive qualitative and quantitative evaluations using cat videos. Wealso demonstrate versatility across unseen cats and indoor environments,producing temporally coherent 4D outputs for enriched virtual experiences.

Virtual Pets: Animatable Animal Generation in 3D Scenes