InHabit: Leveraging Image Foundation Models for Scalable 3D Human Placement
Researchers have developed InHabit, a novel system for generating large-scale, photorealistic 3D datasets of humans interacting with environments. This system leverages image foundation models to propose actions and insert humans into 3D scenes, then refines these insertions into physically plausible SMPL-X bodies. The resulting dataset, InHabitants, comprises 78,000 samples across approximately 800 scenes and has demonstrated improvements in 3D human-scene reconstruction and contact estimation tasks. AI