NCRF: Neural Contact Radiance Fields for Free-Viewpoint Rendering of Hand-Object Interaction

• URL: http://arxiv.org/abs/2402.05532v2
• Date: Fri, 9 Feb 2024 13:00:22 GMT
• Title: NCRF: Neural Contact Radiance Fields for Free-Viewpoint Rendering of Hand-Object Interaction
• Authors: Zhongqun Zhang and Jifei Song and Eduardo P\'erez-Pellitero and Yiren Zhou and Hyung Jin Chang and Ale\v{s} Leonardis
• Abstract summary: We present a novel free-point rendering framework, Neural Contact Radiance Field ( NCRF), to reconstruct hand-object interactions from a sparse set of videos. We jointly learn these key components where they mutually help and regularize each other with visual and geometric constraints. Our approach outperforms the current state-of-the-art in terms of both rendering quality and pose estimation accuracy.
• Score: 19.957593804898064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling hand-object interactions is a fundamentally challenging task in 3D computer vision. Despite remarkable progress that has been achieved in this field, existing methods still fail to synthesize the hand-object interaction photo-realistically, suffering from degraded rendering quality caused by the heavy mutual occlusions between the hand and the object, and inaccurate hand-object pose estimation. To tackle these challenges, we present a novel free-viewpoint rendering framework, Neural Contact Radiance Field (NCRF), to reconstruct hand-object interactions from a sparse set of videos. In particular, the proposed NCRF framework consists of two key components: (a) A contact optimization field that predicts an accurate contact field from 3D query points for achieving desirable contact between the hand and the object. (b) A hand-object neural radiance field to learn an implicit hand-object representation in a static canonical space, in concert with the specifically designed hand-object motion field to produce observation-to-canonical correspondences. We jointly learn these key components where they mutually help and regularize each other with visual and geometric constraints, producing a high-quality hand-object reconstruction that achieves photo-realistic novel view synthesis. Extensive experiments on HO3D and DexYCB datasets show that our approach outperforms the current state-of-the-art in terms of both rendering quality and pose estimation accuracy.

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