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arXiv · 2603.12063

NBAvatar: Neural Billboards Avatars with Realistic Hand-Face Interaction

David Svitov·Mahtab Dahaghin
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Abstract

We present NBAvatar - a method for realistic rendering of head avatars handling non-rigid deformations caused by hand-face interaction. We introduce a novel representation for animated avatars by combining the training of oriented planar primitives with neural rendering. Such a combination of explicit and implicit representations enables NBAvatar to handle temporally and pose-consistent geometry, along with fine-grained appearance details provided by the neural rendering technique. In our experiments, we demonstrate that NBAvatar implicitly learns color transformations caused by face-hand interactions and surpasses existing approaches in terms of novel-view and novel-pose rendering quality. Specifically, NBAvatar achieves up to 30% LPIPS reduction under high-resolution megapixel rendering compared to Gaussian-based avatar methods, while also improving PSNR and SSIM, and achieves higher structural similarity compared to the state-of-the-art hand-face interaction method InteractAvatar.

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David Svitov, Mahtab Dahaghin. 2026-03-12. NBAvatar: Neural Billboards Avatars with Realistic Hand-Face Interaction. https://arxiv.org/abs/2603.12063

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