NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation
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 https://yuzeng-at-tri.github.io/ppd-page/{project page}.
69324f7a6d1060ca587a2729
Yu Zeng
69324f7a6d1060ca587a272a
Charles Ochoa
69324f7a6d1060ca587a272b
Mingyuan Zhou
69324f7a6d1060ca587a272c
Vishal M. Patel
69324f7a6d1060ca587a272d
Vitor Guizilini
69324f7a6d1060ca587a272e
Rowan McAllister
NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation
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 https://yuzeng-at-tri.github.io/ppd-page/{project page}.
Phase-Preserving Diffusion and Frequency-Selective Structured noise enable structure-aligned generation in diffusion models without altering architecture or introducing extra parameters, enhancing performance in tasks like re-rendering and simulation.
diffusion Gaussian noise Fourier coefficients geometric consistency Phase-Preserving Diffusion φ-PD Frequency-Selective Structured noise structural rigidity frequency-cutoff inference-time cost image-to-image generation video-to-video generation CARLA-to-Waymo planner