Neural crossbreed

Park, Sanghun; Seo, Kwanggyoon; Noh, Junyong

doi:10.1145/3414685.3417797

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…We used Neural Crossbreed [36] as a morphing algorithm for creating our stimulus set. This neural network is particularly well-suited to, and pre-trained on, morphing between dog images.…”

Section: Methodsmentioning

confidence: 99%

Modelling individual aesthetic judgements over time

Brielmann,

Berentelg,

Dayan

2023

Phil. Trans. R. Soc. B

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Listening to music, watching a sunset—many sensory experiences are valuable to us, to a degree that differs significantly between individuals, and within an individual over time. We have theorized (Brielmann & Dayan 2022 Psychol. Rev . 129 , 1319–1337 ( doi:10.1037/rev0000337 ))) that these idiosyncratic values derive from the task of using experiences to tune the sensory-cognitive system to current and likely future input. We tested the theory using participants’ ( n = 59) ratings of a set of dog images ( n = 55) created using the NeuralCrossbreed morphing algorithm. A full realization of our model that uses feature representations extracted from image-recognizing deep neural nets (e.g. VGG-16) is able to capture liking judgements on a trial-by-trial basis (median r = 0.65), outperforming predictions based on population averages (median r = 0.01). Furthermore, the model’s learning component allows it to explain image sequence dependent rating changes, capturing on average 17% more variance in the ratings for the true trial order than for simulated random trial orders. This validation of our theory is the first step towards a comprehensive treatment of individual differences in evaluation. This article is part of the theme issue ‘Art, aesthetics and predictive processing: theoretical and empirical perspectives’.

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“…We used Neural Crossbreed [36] as a morphing algorithm for creating our stimulus set. This neural network is particularly well-suited to, and pre-trained on, morphing between dog images.…”

Section: Methodsmentioning

confidence: 99%

Modelling individual aesthetic judgements over time

Brielmann,

Berentelg,

Dayan

2023

Phil. Trans. R. Soc. B

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show abstract

“…For image‐based approaches, interpolation turns into an image morphing (i.e., deformation and blending) problem. Many solutions have been proposed for photographs (e.g., [FZP*20, PSN20]), cartoon animations [LZLS21, SZY*21, CZ22] and, closest to our inputs, concept sketches [ADN*17]. Yet, rough drawings have a very specific style which requires preserving the distribution, spatial continuity and color or gray‐level intensity of the strokes.…”

Section: Related Workmentioning

confidence: 99%

Non‐linear Rough 2D Animation using Transient Embeddings

Even,

Bénard,

Barla

2023

Computer Graphics Forum

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Traditional 2D animation requires time and dedication since tens of thousands of frames need to be drawn by hand for a typical production. Many computer‐assisted methods have been proposed to automatize the generation of inbetween frames from a set of clean line drawings, but they are all limited by a rigid workflow and a lack of artistic controls, which is in the most part due to the one‐to‐one stroke matching and interpolation problems they attempt to solve. In this work, we take a novel view on those problems by focusing on an earlier phase of the animation process that uses rough drawings (i.e., sketches). Our key idea is to recast the matching and interpolation problems so that they apply to transient embeddings, which are groups of strokes that only exist for a few keyframes. A transient embedding carries strokes between keyframes both forward and backward in time through a sequence of transformed lattices. Forward and backward strokes are then cross‐faded using their thickness to yield rough inbetweens. With our approach, complex topological changes may be introduced while preserving visual motion continuity. As demonstrated on state‐of‐the‐art 2D animation exercises, our system provides unprecedented artistic control through the non‐linear exploration of movements and dynamics in real‐time.

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“…We used Neural Crossbreed (Park et al, 2020) as a morphing algorithm for creating our stimulus set. This neural network is particularly well-suited to, and pre-trained on, morphing between dog images.…”

Section: Stimulimentioning

confidence: 99%

Modeling individual aesthetic judgments over time

Brielmann¹,

Dayan²,

Berentelg³

2023

Preprint

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Listening to music, watching a sunset — many sensory experiences are valuable to us, toa degree that differs signiﬁcantly between individuals, and within an individual over time.We have theorized (Brielmann & Dayan, Psychological Review, 2022) that these idiosyncraticvalues derive from the task of using experiences to tune the sensory system to current andlikely future input. We tested the theory using participants’ (N = 59) ratings of a set of dogimages (n = 55) created using the NeuralCrossbreed morphing algorithm. A full realizationof our model that uses feature representations extracted from image-recognizing deep neuralnets (e.g., VGG-16) is able to capture liking judgments on a trial-by-trial basis (median r = 0.65),outperforming predictions based on population averages (median r = 0.01). Furthermore, themodel’s learning component allows it to explain image sequence dependent rating changes,capturing on average 17% more variance in the ratings for the true trial order than for simulatedrandom trial orders. This validation of our theory is the ﬁrst step towards a comprehensivetreatment of individual differences in evaluation.

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Neural crossbreed

Cited by 6 publications

References 27 publications

Modelling individual aesthetic judgements over time

Modelling individual aesthetic judgements over time

Non‐linear Rough 2D Animation using Transient Embeddings

Modeling individual aesthetic judgments over time

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