InterPet4D: A Multimodal 4D Human-Pet Interaction Dataset for Pet Motion Generation

Human-pet interaction estimation and generation remain underexplored due to the absence of high-quality large-scale dataset. We present InterPet4D, the first multimodal dataset capturing natural interactions between humans and dogs. Using a synchronized multi-view capture system, we record human–dog obedience tasks and provide annotations for both humans and dogs, including multiview and egocentric videos, segmentations, 2D/3D keypoints, meshes, and audio tracks. Interpet4D consists of 6.8 million frames collected from 13 dogs of 11 breeds interacting with 23 human participants. We further introduce the InterPetMoGen framework for human-pet interaction motion generation. Our proposed model achieves an FID score of 11.21, substantially outperforms the Seq2Seq or DiT baselines, demonstrating the effectiveness of Interpet4D for modeling realistic human–pet interactions.