  We present InterHandGen, a novel framework that learns the generative prior
of two-hand interaction. Sampling from our model yields plausible and diverse
two-hand shapes in close interaction with or without an object. Our prior can
be incorporated into any optimization or learning methods to reduce ambiguity
in an ill-posed setup. Our key observation is that directly modeling the joint
distribution of multiple instances imposes high learning complexity due to its
combinatorial nature. Thus, we propose to decompose the modeling of joint
distribution into the modeling of factored unconditional and conditional single
instance distribution. In particular, we introduce a diffusion model that
learns the single-hand distribution unconditional and conditional to another
hand via conditioning dropout. For sampling, we combine anti-penetration and
classifier-free guidance to enable plausible generation. Furthermore, we
establish the rigorous evaluation protocol of two-hand synthesis, where our
method significantly outperforms baseline generative models in terms of
plausibility and diversity. We also demonstrate that our diffusion prior can
boost the performance of two-hand reconstruction from monocular in-the-wild
images, achieving new state-of-the-art accuracy.
