IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models at ICLR 2024

Core Concepts

Diffusion-based image morphing approach IMPUS achieves smooth, direct, and realistic interpolations.

Abstract

The paper introduces IMPUS, a diffusion-based image morphing approach that focuses on producing smooth, direct, and realistic interpolations between two images. The method utilizes perceptually-uniform sampling techniques and model adaptation to control the quality of the morphed images. By optimizing text embeddings and mapping images to latent spaces using probability flow ODEs, IMPUS aims to bridge the gap between distinct conditioned distributions in image pairs. The heuristic bottleneck constraint ensures a balance between diversity and directness in the morphing process. Extensive experiments validate the effectiveness of IMPUS in achieving high-quality image morphing results adaptable to various generative tasks.

Stats

arXiv:2311.06792v2 [cs.CV] 16 Mar 2024

Quotes

"We propose a robust open world automatic image morphing framework with diffusion models."
"We present an algorithm for perceptually-uniform sampling that encourages visually smooth changes."
"Our approach achieves better performance on smoothness, directness, and realism."

Key Insights Distilled From

IMPUS

by Zhaoyuan Yan... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2311.06792.pdf

Deeper Inquiries

How can IMPUS be applied to other generative tasks beyond image morphing

IMPUS can be applied to other generative tasks beyond image morphing by leveraging its diffusion-based approach with perceptually-uniform sampling. One potential application is in data augmentation, where IMPUS can generate novel and diverse samples for training datasets. This can help improve the generalization and robustness of machine learning models. Additionally, IMPUS can be used for model evaluation and explainability by generating interpolated images that provide insights into model predictions and decision-making processes. Another application is in video interpolation, where IMPUS can generate realistic intermediate frames between two consecutive video frames, enhancing the visual quality of videos.

What are potential limitations or drawbacks of using diffusion models for image manipulation

While diffusion models like IMPUS have shown promising results in image manipulation tasks, there are some limitations or drawbacks to consider:

Computational Complexity: Training diffusion models can be computationally intensive due to the iterative nature of the optimization process.
Interpretability: Diffusion models may lack interpretability compared to traditional methods like GANs, making it challenging to understand how changes in input parameters affect output images.
Mode Collapse: Like other generative models, diffusion models are susceptible to mode collapse, where they fail to capture the full diversity of a dataset leading to repetitive outputs.
Training Data Dependency: Diffusion models require large amounts of high-quality training data for effective performance, which may limit their applicability in scenarios with limited or low-quality data.

How does the concept of relative perceptual path diversity impact the overall quality of the morphed images

The concept of relative perceptual path diversity plays a crucial role in determining the overall quality of morphed images generated by IMPUS. By controlling sample diversity along the morphing path relative to the diversity at endpoints through heuristic bottleneck constraints based on rPPD scores, IMPUS ensures a balance between directness and fidelity in image transitions. A higher rPPD score indicates greater sample diversity along the path compared to endpoint differences, leading to smoother transitions with less variation across interpolated images while maintaining realism and directness criteria essential for high-quality image morphing results.