Manuel Lagunas

I am an applied scientist at Amazon in Madrid, where I work on developing computer vision and machine learning techniques to solve catalog issues. Before, I did my PhD at Universidad de Zaragoza, where I was advised by Diego Gutierrez and Belen Masia. During my PhD, I was working on problems at the interface between computer vision, computer graphics, and human perception.

You can contact me at mlgns at amazon dot com

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I am interested in topics at the interface between computer vision and computer graphics. Those include - but are not limited to - how can we inversely model the world i.e. acquire material properties, light, or geometry from simple input sources (images); or how can we develop faster/ more intuitive methods to manipulate digital assets or foster artistic processes.

You can find my PhD thesis highlighted.

We perform in-the-wild intuitive material editing using perceptual attributes. We recover high frequency details from the input while mantaining the intuitive editing capacity of the model.

We rely on an estimation of the geometry from the input image and an editing network that uses high-level perceptual attributes to perform intuitive material editing.

This thesis improves visual content creation algorithms by connecting physical parameters to intuitive human attributes related to visual appearance.

We train a generative model to perform in-the-wild human relighting lifting the assumptions on materials being Lambertian.

Comprehensive study on the influence of geometry, illumination, and their frequencies in our performance recognizing materials from images.

Analysis of subjective and objective measures when we develop computational methods for material similarity.

We introduce a neural-based similarity metric that learns from perceptual data. It outperforms state of the art, is aligned with human perception, and can be used for several applications.

Comprehensive study of the effect of motion in our perception of high-level perceptual attributes that describe material appearance.

We introduce a similarity model capable of retrieving icons based on their style and visual identity. We rely on a siamese model paired with a triplet loss function that learns from crowd-sourced data.

We develop a transfer learning method that fine tunes the initial layers of a convolutional neural network. This allows it to learn low-level features (strokes) wich are important in illustrations.

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