Towards High Fidelity Face Frontalization in the Wild

Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper prop...
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Autor*in:	Cao, Jie [verfasserIn] Hu, Yibo [verfasserIn] Zhang, Hongwen [verfasserIn] He, Ran [verfasserIn] Sun, Zhenan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Face frontalization Realistic face generation Pose-invariant face recognition

Übergeordnetes Werk:	Enthalten in: International journal of computer vision - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 128(2019), 5 vom: 12. Okt., Seite 1485-1504
Übergeordnetes Werk:	volume:128 ; year:2019 ; number:5 ; day:12 ; month:10 ; pages:1485-1504

Links:	Volltext

DOI / URN:	10.1007/s11263-019-01229-6

Katalog-ID:	SPR039634930

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520			\|a Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances.
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allfields	10.1007/s11263-019-01229-6 doi (DE-627)SPR039634930 (SPR)s11263-019-01229-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Cao, Jie verfasserin aut Towards High Fidelity Face Frontalization in the Wild 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances. Face frontalization (dpeaa)DE-He213 Realistic face generation (dpeaa)DE-He213 Pose-invariant face recognition (dpeaa)DE-He213 Hu, Yibo verfasserin aut Zhang, Hongwen verfasserin aut He, Ran verfasserin aut Sun, Zhenan verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 128(2019), 5 vom: 12. Okt., Seite 1485-1504 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:128 year:2019 number:5 day:12 month:10 pages:1485-1504 https://dx.doi.org/10.1007/s11263-019-01229-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 128 2019 5 12 10 1485-1504
spelling	10.1007/s11263-019-01229-6 doi (DE-627)SPR039634930 (SPR)s11263-019-01229-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Cao, Jie verfasserin aut Towards High Fidelity Face Frontalization in the Wild 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances. Face frontalization (dpeaa)DE-He213 Realistic face generation (dpeaa)DE-He213 Pose-invariant face recognition (dpeaa)DE-He213 Hu, Yibo verfasserin aut Zhang, Hongwen verfasserin aut He, Ran verfasserin aut Sun, Zhenan verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 128(2019), 5 vom: 12. Okt., Seite 1485-1504 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:128 year:2019 number:5 day:12 month:10 pages:1485-1504 https://dx.doi.org/10.1007/s11263-019-01229-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 128 2019 5 12 10 1485-1504
allfields_unstemmed	10.1007/s11263-019-01229-6 doi (DE-627)SPR039634930 (SPR)s11263-019-01229-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Cao, Jie verfasserin aut Towards High Fidelity Face Frontalization in the Wild 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances. Face frontalization (dpeaa)DE-He213 Realistic face generation (dpeaa)DE-He213 Pose-invariant face recognition (dpeaa)DE-He213 Hu, Yibo verfasserin aut Zhang, Hongwen verfasserin aut He, Ran verfasserin aut Sun, Zhenan verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 128(2019), 5 vom: 12. Okt., Seite 1485-1504 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:128 year:2019 number:5 day:12 month:10 pages:1485-1504 https://dx.doi.org/10.1007/s11263-019-01229-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 128 2019 5 12 10 1485-1504
allfieldsGer	10.1007/s11263-019-01229-6 doi (DE-627)SPR039634930 (SPR)s11263-019-01229-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Cao, Jie verfasserin aut Towards High Fidelity Face Frontalization in the Wild 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances. Face frontalization (dpeaa)DE-He213 Realistic face generation (dpeaa)DE-He213 Pose-invariant face recognition (dpeaa)DE-He213 Hu, Yibo verfasserin aut Zhang, Hongwen verfasserin aut He, Ran verfasserin aut Sun, Zhenan verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 128(2019), 5 vom: 12. Okt., Seite 1485-1504 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:128 year:2019 number:5 day:12 month:10 pages:1485-1504 https://dx.doi.org/10.1007/s11263-019-01229-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 128 2019 5 12 10 1485-1504
allfieldsSound	10.1007/s11263-019-01229-6 doi (DE-627)SPR039634930 (SPR)s11263-019-01229-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.74 bkl Cao, Jie verfasserin aut Towards High Fidelity Face Frontalization in the Wild 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances. Face frontalization (dpeaa)DE-He213 Realistic face generation (dpeaa)DE-He213 Pose-invariant face recognition (dpeaa)DE-He213 Hu, Yibo verfasserin aut Zhang, Hongwen verfasserin aut He, Ran verfasserin aut Sun, Zhenan verfasserin aut Enthalten in International journal of computer vision Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 128(2019), 5 vom: 12. Okt., Seite 1485-1504 (DE-627)271350083 (DE-600)1479903-0 1573-1405 nnns volume:128 year:2019 number:5 day:12 month:10 pages:1485-1504 https://dx.doi.org/10.1007/s11263-019-01229-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.74 ASE AR 128 2019 5 12 10 1485-1504
language	English
source	Enthalten in International journal of computer vision 128(2019), 5 vom: 12. Okt., Seite 1485-1504 volume:128 year:2019 number:5 day:12 month:10 pages:1485-1504
sourceStr	Enthalten in International journal of computer vision 128(2019), 5 vom: 12. Okt., Seite 1485-1504 volume:128 year:2019 number:5 day:12 month:10 pages:1485-1504
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Face frontalization Realistic face generation Pose-invariant face recognition
dewey-raw	004
isfreeaccess_bool	false
container_title	International journal of computer vision
authorswithroles_txt_mv	Cao, Jie @@aut@@ Hu, Yibo @@aut@@ Zhang, Hongwen @@aut@@ He, Ran @@aut@@ Sun, Zhenan @@aut@@
publishDateDaySort_date	2019-10-12T00:00:00Z
hierarchy_top_id	271350083
dewey-sort	14
id	SPR039634930
language_de	englisch
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author	Cao, Jie
spellingShingle	Cao, Jie ddc 004 bkl 54.74 misc Face frontalization misc Realistic face generation misc Pose-invariant face recognition Towards High Fidelity Face Frontalization in the Wild
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topic_title	004 ASE 54.74 bkl Towards High Fidelity Face Frontalization in the Wild Face frontalization (dpeaa)DE-He213 Realistic face generation (dpeaa)DE-He213 Pose-invariant face recognition (dpeaa)DE-He213
topic	ddc 004 bkl 54.74 misc Face frontalization misc Realistic face generation misc Pose-invariant face recognition
topic_unstemmed	ddc 004 bkl 54.74 misc Face frontalization misc Realistic face generation misc Pose-invariant face recognition
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title	Towards High Fidelity Face Frontalization in the Wild
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title_full	Towards High Fidelity Face Frontalization in the Wild
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title_sort	towards high fidelity face frontalization in the wild
title_auth	Towards High Fidelity Face Frontalization in the Wild
abstract	Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances.
abstractGer	Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances.
abstract_unstemmed	Abstract Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful high-resolution results meanwhile preserve texture details. This paper proposes a high fidelity pose in-variant model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture fusion warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose adversarial residual dictionary learning to supervise facial texture map recovering with only monocular images. Furthermore, a multi-perception guided loss is proposed to address the practical misalignment between the ground truth frontal and profile faces, allowing HF-PIM to effectively utilize multiple images during training. Quantitative and qualitative evaluations on five controlled and uncontrolled databases show that the proposed method not only boosts the performance of pose-invariant face recognition but also improves the visual quality of high-resolution frontalization appearances.
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title_short	Towards High Fidelity Face Frontalization in the Wild
url	https://dx.doi.org/10.1007/s11263-019-01229-6
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up_date	2024-07-04T00:51:23.503Z
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