NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation

• URL: http://arxiv.org/abs/2512.05106v2
• Date: Sun, 07 Dec 2025 09:41:35 GMT
• Title: NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation
• Authors: Yu Zeng, Charles Ochoa, Mingyuan Zhou, Vishal M. Patel, Vitor Guizilini, Rowan McAllister,
• Abstract summary: Phase-Preserving Diffusion -PD is a model-agnostic reformulation of the diffusion process.<n>-PD preserves input phase while randomizing magnitude, enabling structure-aligned generation without architectural changes.<n>-PD adds no inference-time cost and is compatible with any diffusion model for images or videos.
• Score: 88.09231548061295
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Standard diffusion corrupts data using Gaussian noise whose Fourier coefficients have random magnitudes and random phases. While effective for unconditional or text-to-image generation, corrupting phase components destroys spatial structure, making it ill-suited for tasks requiring geometric consistency, such as re-rendering, simulation enhancement, and image-to-image translation. We introduce Phase-Preserving Diffusion φ-PD, a model-agnostic reformulation of the diffusion process that preserves input phase while randomizing magnitude, enabling structure-aligned generation without architectural changes or additional parameters. We further propose Frequency-Selective Structured (FSS) noise, which provides continuous control over structural rigidity via a single frequency-cutoff parameter. φ-PD adds no inference-time cost and is compatible with any diffusion model for images or videos. Across photorealistic and stylized re-rendering, as well as sim-to-real enhancement for driving planners, φ-PD produces controllable, spatially aligned results. When applied to the CARLA simulator, φ-PD improves CARLA-to-Waymo planner performance by 50\%. The method is complementary to existing conditioning approaches and broadly applicable to image-to-image and video-to-video generation. Videos, additional examples, and code are available on our \href{https://yuzeng-at-tri.github.io/ppd-page/}{project page}.

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