Bodies in Society

Our bodies say something about us. We have names for body shapes like "hourglass" or "pear shaped" []. People also make social judgments based on body shape. To understand this we have developed methods to relate linguistic descriptions of people to 3D body shape [].

Linguistic descriptions of body shape are naturally used to convey the physical appearance of people to others. For example, writers aim to create an image of a fictional character in the reader's mind with words, and crime witnesses are asked to verbally describe the appearance of the suspect to a sketch artist. Can we use this same procedure to recreate 3D human body shape? Do we share an understanding of 3D meaning of these shape descriptions?

We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape (for example, 'big', 'fit', 'feminine' or 'pear shape'). We also use non-shape words like 'ignorant', 'liberal', or 'Republican'. We then learn a function relating these ratings to 3D human shape parameters.

Given an image of a person, we use crowdsourcing to collect linguistic ratings of their body shape; this provides strong constraints on their 3D shape. These "crowdshaped" bodies are perceptually indistinguishable from bodies created from high-resolution scans and their metric accuracy is sufficient for many tasks.

Our model provides insight into social biases [] based on body shape. In particular, in the absence of other information, people make judgements about leadership traits based only on body shape []. Voters find certain body shapes to be more trustworthy, more leader-like, more compassionate, or more conservative.

These findings can help raise awareness of unconscious biases in trait perception from body shape. Awareness is the first step in overcoming such biases that can affect decision making in elections, healthcare, the legal system, and education.

Members

Perceiving Systems

Maria Alejandra Quiros-Ramirez

Guest Scientist

Perceiving Systems

Stephan Streuber

Perceiving Systems

Silvia Zuffi

Guest Scientist

Perceiving Systems

Matt Hill

Intern

Perceiving Systems

Michael Black

Emeritus / Acting Director

Perceiving Systems

Naureen Mahmood

Research Engineer

Publications

Perceiving Systems Article Red shape, blue shape: Political ideology influences the social perception of body shape Quiros-Ramirez, M. A., Streuber, S., Black, M. J. Nature Humanities and Social Sciences Communications, 8:148, June 2021 (Published)

Abstract ›

Political elections have a profound impact on individuals and societies. Optimal voting is thought to be based on informed and deliberate decisions yet, it has been demonstrated that the outcomes of political elections are biased by the perception of candidates’ facial features and the stereotypical traits voters attribute to these. Interestingly, political identification changes the attribution of stereotypical traits from facial features. This study explores whether the perception of body shape elicits similar effects on political trait attribution and whether these associations can be visualized. In Experiment 1, ratings of 3D body shapes were used to model the relationship between perception of 3D body shape and the attribution of political traits such as ‘Republican’, ‘Democrat’, or ‘Leader’. This allowed analyzing and visualizing the mental representations of stereotypical 3D body shapes associated with each political trait. Experiment 2 was designed to test whether political identification of the raters affected the attribution of political traits to different types of body shapes. The results show that humans attribute political traits to the same body shapes differently depending on their own political preference. These findings show that our judgments of others are influenced by their body shape and our own political views. Such judgments have potential political and societal implications.

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Perceiving Systems Article Creating body shapes from verbal descriptions by linking similarity spaces Hill, M. Q., Streuber, S., Hahn, C. A., Black, M. J., O’Toole, A. J. Psychological Science, 27(11):1486-1497, November 2016

Abstract ›

Brief verbal descriptions of bodies (e.g. curvy, long-legged) can elicit vivid mental images. The ease with which we create these mental images belies the complexity of three-dimensional body shapes. We explored the relationship between body shapes and body descriptions and show that a small number of words can be used to generate categorically accurate representations of three-dimensional bodies. The dimensions of body shape variation that emerged in a language-based similarity space were related to major dimensions of variation computed directly from three-dimensional laser scans of 2094 bodies. This allowed us to generate three-dimensional models of people in the shape space using only their coordinates on analogous dimensions in the language-based description space. Human descriptions of photographed bodies and their corresponding models matched closely. The natural mapping between the spaces illustrates the role of language as a concise code for body shape, capturing perceptually salient global and local body features.

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Perceiving Systems Article Body Talk: Crowdshaping Realistic 3D Avatars with Words Streuber, S., Quiros-Ramirez, M. A., Hill, M. Q., Hahn, C. A., Zuffi, S., O’Toole, A., Black, M. J. ACM Trans. Graph. (Proc. SIGGRAPH), 35(4):54:1-54:14, July 2016

Abstract ›

Realistic, metrically accurate, 3D human avatars are useful for games, shopping, virtual reality, and health applications. Such avatars are not in wide use because solutions for creating them from high-end scanners, low-cost range cameras, and tailoring measurements all have limitations. Here we propose a simple solution and show that it is surprisingly accurate. We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape. We then learn a linear function relating these ratings to 3D human shape parameters. Given an image of a new body, we again turn to the crowd for ratings of the body shape. The collection of linguistic ratings of a photograph provides remarkably strong constraints on the metric 3D shape. We call the process crowdshaping and show that our Body Talk system produces shapes that are perceptually indistinguishable from bodies created from high-resolution scans and that the metric accuracy is sufficient for many tasks. This makes body “scanning” practical without a scanner, opening up new applications including database search, visualization, and extracting avatars from books.

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