GSHOI Denoiser: Denoising Gaussian Hand-Object Interaction for Photorealistic Rendering

Many VR/AR applications require the photorealistic rendering of hand-object interactions. Virtual hands are driven by users' hand poses captured via motion tracking to interact with virtual objects. The driven pose can be very noisy due to the constraints of tracking hardware and computation accuracy. This noise may lead to distorted hand poses and penetration artifacts during rendering. In this paper, we introduce the Gaussian Hand-Object Interaction Denoiser, the Gaussian splatting-based hand-object interaction denoising method, which effectively denoises the input twisted and penetrated hand poses to produce photorealistic results. We first propose the innovative joint-to-Gaussian surface representation, which accurately models the spatial relationships between hand skeleton joints and object Gaussians while highlighting hand-object penetrations and generalizing well to new hand poses and objects. Then, we propose a geometry-aware de-penetration algorithm that eliminates penetrations by detecting intersections between skeleton bones and object Gaussians and reposing any penetrated fingers onto the estimated underlying surface of the object. Experiments demonstrate that our method not only effectively reduces hand-object penetration depth but also produces more realistic rendering quality compared to the state-of-the-art methods MANUS+GEARS, MANUS+GeneOH, and 2 DGS+Gene OH. The user study results show that our method significantly improves the users' visual perceptual experience regarding penetration and stability metrics. Project page: https://github.com/ZhaoLizz/GSHOIDenoiser