Closely Interacting 3D Clothed Human Generation with a Tightness Signal

We introduce interacting clothed human generation, generating a pair of 3D clothed humans in close contact such as hugging, holding hands, or dancing, from text and a target interaction pose. Existing work either generates interacting bodies without clothing or reconstructs clothed people in contact from a real image, while single clothed-human generators produce an isolated person with limited text control and fidelity. No prior method generates novel interacting clothed humans. We propose a training-free pipeline that generates each clothed person in 2D, so a text prompt controls diverse subjects and outfits at high fidelity. It then reaches the interaction pose in 3D for exact contact, and resolves the garment interpenetration. The tightness signal drives both the reposing and the penetration solution through one confidence-weighted variational solver. First, tightness-aware skinning uses the signal's direction to recover cloth-to-body correspondences and its magnitude as a per-vertex confidence in the variational solver for the skinning weights, enabling template-free reposing without the loose-garment artifacts of naive weight transfer. Second, penetration-aware combination solves for a smooth per-vertex displacement field that pushes interpenetrating garments apart without distorting them, with the tightness signal making the looser garment yield more. In the experiments on diverse datasets, our reposing lowers cloth distortion on every measure, reducing out-of-plane bending by 5.6× over naive skinning, and our combination removes 94% of the interpenetration volume without distorting the garments. As a complete pipeline, it generates diverse, text-driven pairs of interacting clothed humans whose individual people surpass state-of-the-art single-person generators in appearance and geometry.