Causal Representation Learning for Fine-Grained Face Transfer
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Identity transfer often faces the challenge of generalizing to new situations where large pose and expression or background gaps exist between source and target face images. To improve generalization in such situations, biases take a key role <cit.>. This paper proposes an Errors-in-Variables Adapter (EVA) model to induce learning of proper generalizations by explicitly employing biases to identity estimation based on prior knowledge about the target situation. To better match the source face with the target situation in terms of pose, expression, and background factors, we model the bias as a causal effect of the target situation on source identity and estimate this effect through a controlled intervention trial. To achieve smoother transfer for the target face across the identity gap, we eliminate the target face specificity through multiple kernel regressions. The kernels are used to constrain the regressions to operate only on identity information in the internal representations of the target image, while leaving other perceptual information invariant. Combining these post-regression representations with the biased estimation for identity, EVA shows impressive performance even in the presence of large gaps, providing empirical evidence supporting the utility of the inductive biases in identity estimation.
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