Learning High-Quality Initial Noise for Single-View Synthesis with Diffusion Models

• URL: http://arxiv.org/abs/2512.16219v1
• Date: Thu, 18 Dec 2025 06:08:21 GMT
• Title: Learning High-Quality Initial Noise for Single-View Synthesis with Diffusion Models
• Authors: Zhihao Zhang, Xuejun Yang, Weihua Liu, Mouquan Shen,
• Abstract summary: In diffusion models, certain high-quality initial noise patterns lead to better generation results than others.<n>We propose a learning framework based on an encoder-decoder network (EDN) that directly transforms random noise into high-quality noise.
• Score: 10.275373477634217
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Single-view novel view synthesis (NVS) models based on diffusion models have recently attracted increasing attention, as they can generate a series of novel view images from a single image prompt and camera pose information as conditions. It has been observed that in diffusion models, certain high-quality initial noise patterns lead to better generation results than others. However, there remains a lack of dedicated learning frameworks that enable NVS models to learn such high-quality noise. To obtain high-quality initial noise from random Gaussian noise, we make the following contributions. First, we design a discretized Euler inversion method to inject image semantic information into random noise, thereby constructing paired datasets of random and high-quality noise. Second, we propose a learning framework based on an encoder-decoder network (EDN) that directly transforms random noise into high-quality noise. Experiments demonstrate that the proposed EDN can be seamlessly plugged into various NVS models, such as SV3D and MV-Adapter, achieving significant performance improvements across multiple datasets. Code is available at: https://github.com/zhihao0512/EDN.

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