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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Yang Song

The University of Tennessee, Knoxville


Zhifei Zhang

The University of Tennessee, Knoxville


Hairong Qi

The University of Tennessee, Knoxville


DOI:

10.1609/aaai.v32i1.11854


Abstract:

Recent face composite and synthesis related works have shown promising results in generating realistic face images from deep convolutional networks. However, these works either do not generate consistent results when the constituent patches contain large domain variations (i.e., from face and sketch domains) or cannot generate high-resolution images with limited facial patches (e.g., the inpainting approach tends to blur the generated region when the missing area is more than 50%). Motivated by the mental imagery and simulation in human cognition, we exploit the potential of deep learning networks in filling large missing region (e.g., as high as 95% missing) and generating realistic faces with high fidelity in cross domains.We propose the recursive generation by bidirectional transformation networks (r-BTN) that recursively generates a whole face/sketch from a small sketch/face patch. The large missing area and domain variations make it difficult to generate satisfactory results using a unidirectional cross-domain learning structure. We explore that the bidirectional transformation network can lead to the consistent result by minimizing the forward and backward errors in the cross-domain scenario. On the other hand, a forward and backward bidirectional learning between the face and sketch domains would enable recursive estimation of the missing region in an incremental manner to yield appealing results. r-BTN also adopts an adversarial constraint to encourage the generation of realistic faces/sketches. Extensive experiments have been conducted to demonstrate the superior performance from r-BTN as compared to existing potential solutions.

Topics: AAAI

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HOW TO CITE:

Yang Song||Zhifei Zhang||Hairong Qi r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Yang Song||Zhifei Zhang||Hairong Qi r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches AAAI 2018, .

Yang Song||Zhifei Zhang||Hairong Qi (2018). r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Yang Song||Zhifei Zhang||Hairong Qi. r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Yang Song||Zhifei Zhang||Hairong Qi. 2018. r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Yang Song||Zhifei Zhang||Hairong Qi. (2018) "r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Yang Song||Zhifei Zhang||Hairong Qi, "r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches", AAAI, p., 2018.

Yang Song||Zhifei Zhang||Hairong Qi. "r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Yang Song||Zhifei Zhang||Hairong Qi. "r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Yang Song||Zhifei Zhang||Hairong Qi. r-BTN: Cross-Domain Face Composite and Synthesis From Limited Facial Patches. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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Artificial Intelligence 1900 Embarcadero Road, Suite
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