PTDiffusion: Free Lunch for Generating Optical Illusion Hidden Pictures with Phase-Transferred Diffusion Model

• URL: http://arxiv.org/abs/2503.06186v5
• Date: Fri, 18 Apr 2025 03:50:36 GMT
• Title: PTDiffusion: Free Lunch for Generating Optical Illusion Hidden Pictures with Phase-Transferred Diffusion Model
• Authors: Xiang Gao, Shuai Yang, Jiaying Liu,
• Abstract summary: Optical illusion hidden picture is an interesting visual phenomenon where an image is cleverly integrated into another picture in a way that is not immediately obvious to the viewer.<n>We propose a novel training-free text-guided image-to-image (I2I) translation framework dubbed as textbfPhase-textbfTransferred textbfDiffusion Model (PTDiffusion) for hidden art syntheses.
• Score: 23.479182559911813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical illusion hidden picture is an interesting visual perceptual phenomenon where an image is cleverly integrated into another picture in a way that is not immediately obvious to the viewer. Established on the off-the-shelf text-to-image (T2I) diffusion model, we propose a novel training-free text-guided image-to-image (I2I) translation framework dubbed as \textbf{P}hase-\textbf{T}ransferred \textbf{Diffusion} Model (PTDiffusion) for hidden art syntheses. PTDiffusion harmoniously embeds an input reference image into arbitrary scenes described by the text prompts, producing illusion images exhibiting hidden visual cues of the reference image. At the heart of our method is a plug-and-play phase transfer mechanism that dynamically and progressively transplants diffusion features' phase spectrum from the denoising process to reconstruct the reference image into the one to sample the generated illusion image, realizing deep fusion of the reference structural information and the textual semantic information in the diffusion model latent space. Furthermore, we propose asynchronous phase transfer to enable flexible control to the degree of hidden content discernability. Our method bypasses any model training and fine-tuning process, all while substantially outperforming related text-guided I2I methods in image generation quality, text fidelity, visual discernibility, and contextual naturalness for illusion picture synthesis, as demonstrated by extensive qualitative and quantitative experiments. Our project is publically available at \href{https://xianggao1102.github.io/PTDiffusion_webpage/}{this web page}.

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