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arXiv · 2412.03407

Skel3D: Skeleton Guided Novel View Synthesis

Aron Fóthi·Bence Fazekas·Natabara Máté Gyöngyössy·Kristian Fenech
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Abstract

In this paper, we present an approach for monocular open-set novel view synthesis (NVS) that leverages object skeletons to guide the underlying diffusion model. Building upon a baseline that utilizes a pre-trained 2D image generator, our method takes advantage of the Objaverse dataset, which includes animated objects with bone structures. By introducing a skeleton guide layer following the existing ray conditioning normalization (RCN) layer, our approach enhances pose accuracy and multi-view consistency. The skeleton guide layer provides detailed structural information for the generative model, improving the quality of synthesized views. Experimental results demonstrate that our skeleton-guided method significantly enhances consistency and accuracy across diverse object categories within the Objaverse dataset. Our method outperforms existing state-of-the-art NVS techniques both quantitatively and qualitatively, without relying on explicit 3D representations.

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Aron Fóthi, Bence Fazekas, Natabara Máté Gyöngyössy, Kristian Fenech. 2024-12-04. Skel3D: Skeleton Guided Novel View Synthesis. https://arxiv.org/abs/2412.03407

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