Researchers have introduced InterPet4D, a novel multimodal dataset designed to advance the study of human-pet interactions, specifically focusing on motion generation. This dataset comprises 6.8 million frames capturing natural interactions between humans and dogs, featuring synchronized multi-view videos, 2D/3D keypoints, meshes, and audio. Alongside the dataset, the team developed the InterPetMoGen framework, which utilizes a Diffusion Transformer architecture. Their model demonstrated superior performance over baseline methods, achieving an FID score of 11.21, highlighting the dataset's utility for realistic human-pet motion modeling. AI
IMPACT This dataset and framework could enable new research and applications in realistic human-pet interaction modeling and animation.
RANK_REASON The cluster describes a new academic dataset and framework for motion generation, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Diffusion Transformer
- Gotit.pub
- Hugging Face
- InterPet4D
- InterPetMoGen
- ScienceCast
- sequence-to-sequence learning
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