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interhandgen interhandgen two hand interaction generation via cascaded reverse diffusion jihyun lee 1 shunsuke saito 2 giljoo nam 2 minhyuk sung 1 tae kyun kim 1 3 kaist 1 codec avatars lab meta 2 imperial college london 3 cvpr 2024 paper arxiv code interhandgen generates two hand interactions with or without an object using a novel cascaded diffusion this generative prior can be incorporated into any optimization or learning methods to reduce ambiguity in an ill posed setup abstract 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 two hand interaction generation interhandgen can generate plausible and diverse two hand interactions please also refer to the paper for quantitative comparisons with baselines where we establish the rigorous evaluation protocol of two hand synthesis ️ two hand object interaction generation the leraning formulation of interhandgen can be easily extended to object conditioned two hand interaction generation it is shown to model plausible and diverse bimanual hand object interactions in the wild two hand reconstruction from rgb interhandgen can be incorporated as a prior into any optimization or learning methods via score distillation sampling like loss it achieves new state of the art reconstruction accuracy on two hand reconstruction from in the wild images learning formula and network architecture our key observation is that directly modeling the joint distribution of multiple instances imposes high learning complexity due to its combinatorial nature thus we reformulate the two hand distribution modeling into the modeling of factored single hand model distribution unconditional and conditional to the other hand such that p_ phi mathbf x _ l mathbf x _ r p_ phi mathbf x _ l p_ phi mathbf x _ r mathbf x _ l to reduce the dimensionality of each generation target after normalizing hand side we jointly learn the resulting unconditional and conditional distributions via conditioning dropout using a single network bibtex inproceedings lee2024interhandgen title interhandgen two hand interaction generation via cascaded reverse diffusion author lee jihyun and saito shunsuke and nam giljoo and sung minhyuk and kim tae kyun booktitle cvpr year 2024 this page was built using the academic project page template which was adopted from the nerfies project page this website is licensed under a creative commons attribution sharealike 4 0 international license