Skel3D: Skeleton Guided Novel View Synthesis

• URL: http://arxiv.org/abs/2412.03407v1
• Date: Wed, 04 Dec 2024 15:45:20 GMT
• Title: Skel3D: Skeleton Guided Novel View Synthesis
• Authors: Aron Fóthi, Bence Fazekas, Natabara Máté Gyöngyössy, Kristian Fenech,
• Abstract summary: We present an approach for monocular open-set novel view (NVS) that leverages object skeletons to guide the underlying diffusion model. Our method outperforms existing state-of-the-art NVS techniques both quantitatively and qualitatively, without relying on explicit 3D representations.
• Score: 0.0
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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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