HuGDiffusion: Generalizable Single-Image Human Rendering via 3D Gaussian Diffusion

• URL: http://arxiv.org/abs/2501.15008v1
• Date: Sat, 25 Jan 2025 01:00:33 GMT
• Title: HuGDiffusion: Generalizable Single-Image Human Rendering via 3D Gaussian Diffusion
• Authors: Yingzhi Tang, Qijian Zhang, Junhui Hou,
• Abstract summary: HuGDiffusion is a learning pipeline to achieve novel view synthesis (NVS) of human characters from single-view input images.<n>We aim to generate the set of 3DGS attributes via a diffusion-based framework conditioned on human priors extracted from a single image.<n>Our HuGDiffusion shows significant performance improvements over the state-of-the-art methods.
• Score: 50.02316409061741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present HuGDiffusion, a generalizable 3D Gaussian splatting (3DGS) learning pipeline to achieve novel view synthesis (NVS) of human characters from single-view input images. Existing approaches typically require monocular videos or calibrated multi-view images as inputs, whose applicability could be weakened in real-world scenarios with arbitrary and/or unknown camera poses. In this paper, we aim to generate the set of 3DGS attributes via a diffusion-based framework conditioned on human priors extracted from a single image. Specifically, we begin with carefully integrated human-centric feature extraction procedures to deduce informative conditioning signals. Based on our empirical observations that jointly learning the whole 3DGS attributes is challenging to optimize, we design a multi-stage generation strategy to obtain different types of 3DGS attributes. To facilitate the training process, we investigate constructing proxy ground-truth 3D Gaussian attributes as high-quality attribute-level supervision signals. Through extensive experiments, our HuGDiffusion shows significant performance improvements over the state-of-the-art methods. Our code will be made publicly available.

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