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Zero-Shot Real Facial Attribute Separation and Transfer at Novel Views

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Computational Visual Media (CVM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14593))

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Abstract

Real-time and zero-shot attribute separation of a given real-face image, allowing attribute transfer and rendering at novel views without the aid of multi-view information, has been demonstrated to be beneficial in real-world scenarios. In this work, we propose an alternating optimization framework and train it on attribute-blending (i.e., unstructured) monocular images. Our framework leverages a pre-trained facial attribute encoder and a 3D-representation face synthesis decoder (e.g., HeadNeRF) to reinforce and guide each other mutually. This allows the facial attribute encoder to better express and separate facial attributes and the face synthesis decoder to render faces with better image similarity and attribute consistency.

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References

  1. Abrevaya, V.F., Wuhrer, S., Boyer, E.: Multilinear autoencoder for 3d face model learning. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 1–9. IEEE (2018)

    Google Scholar 

  2. Athar, S., Xu, Z., Sunkavalli, K., Shechtman, E., Shu, Z.: Rignerf: Fully controllable neural 3d portraits. In: Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition. pp. 20364–20373 (2022)

    Google Scholar 

  3. Bai, Y., Fan, Y., Wang, X., Zhang, Y., Sun, J., Yuan, C., Shan, Y.: High-fidelity facial avatar reconstruction from monocular video with generative priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4541–4551 (2023)

    Google Scholar 

  4. Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: Proceedings of the 26th annual conference on Computer graphics and interactive techniques. pp. 187–194 (1999)

    Google Scholar 

  5. Booth, J., Roussos, A., Ponniah, A., Dunaway, D., Zafeiriou, S.: Large scale 3d morphable models. Int. J. Comput. Vision 126(2), 233–254 (2018)

    Article  MathSciNet  Google Scholar 

  6. Booth, J., Roussos, A., Zafeiriou, S., Ponniah, A., Dunaway, D.: A 3d morphable model learnt from 10,000 faces. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5543–5552 (2016)

    Google Scholar 

  7. Bottou, L.: Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade: Second Edition pp. 421–436 (2012)

    Google Scholar 

  8. Bühler, M.C., Meka, A., Li, G., Beeler, T., Hilliges, O.: Varitex: Variational neural face textures. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 13890–13899 (2021)

    Google Scholar 

  9. Cao, C., Weng, Y., Zhou, S., Tong, Y., Zhou, K.: Facewarehouse: A 3d facial expression database for visual computing. IEEE Trans. Visual Comput. Graphics 20(3), 413–425 (2013)

    Google Scholar 

  10. Chan, E.R., Lin, C.Z., Chan, M.A., Nagano, K., Pan, B., De Mello, S., Gallo, O., Guibas, L.J., Tremblay, J., Khamis, S., et al.: Efficient geometry-aware 3d generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16123–16133 (2022)

    Google Scholar 

  11. Chan, E.R., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G.: pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5799–5809 (2021)

    Google Scholar 

  12. Daněček, R., Black, M.J., Bolkart, T.: Emoca: Emotion driven monocular face capture and animation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20311–20322 (2022)

    Google Scholar 

  13. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39(1), 1–22 (1977)

    MathSciNet  Google Scholar 

  14. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4690–4699 (2019)

    Google Scholar 

  15. Deng, Y., Wang, B., Shum, H.Y.: Learning detailed radiance manifolds for high-fidelity and 3d-consistent portrait synthesis from monocular image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4423–4433 (2023)

    Google Scholar 

  16. Deng, Y., Yang, J., Chen, D., Wen, F., Tong, X.: Disentangled and controllable face image generation via 3d imitative-contrastive learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5154–5163 (2020)

    Google Scholar 

  17. Deng, Y., Yang, J., Xiang, J., Tong, X.: Gram: Generative radiance manifolds for 3d-aware image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10673–10683 (2022)

    Google Scholar 

  18. Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., Tong, X.: Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 0–0 (2019)

    Google Scholar 

  19. Drobyshev, N., Chelishev, J., Khakhulin, T., Ivakhnenko, A., Lempitsky, V., Zakharov, E.: Megaportraits: One-shot megapixel neural head avatars. In: Proceedings of the 30th ACM International Conference on Multimedia. pp. 2663–2671 (2022)

    Google Scholar 

  20. Egger, B., Schönborn, S., Schneider, A., Kortylewski, A., Morel-Forster, A., Blumer, C., Vetter, T.: Occlusion-aware 3d morphable models and an illumination prior for face image analysis. Int. J. Comput. Vision 126, 1269–1287 (2018)

    Article  Google Scholar 

  21. Feng, Y., Feng, H., Black, M.J., Bolkart, T.: Learning an animatable detailed 3D face model from in-the-wild images. vol. 40 (2021), https://doi.org/10.1145/3450626.3459936

  22. Gafni, G., Thies, J., Zollhofer, M., Nießner, M.: Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8649–8658 (2021)

    Google Scholar 

  23. Galanakis, S., Gecer, B., Lattas, A., Zafeiriou, S.: 3dmm-rf: Convolutional radiance fields for 3d face modeling. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 3536–3547 (2023)

    Google Scholar 

  24. Gerig, T., Morel-Forster, A., Blumer, C., Egger, B., Luthi, M., Schönborn, S., Vetter, T.: Morphable face models-an open framework. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). pp. 75–82. IEEE (2018)

    Google Scholar 

  25. Ghosh, P., Gupta, P.S., Uziel, R., Ranjan, A., Black, M.J., Bolkart, T.: Gif: Generative interpretable faces. In: 2020 International Conference on 3D Vision (3DV). pp. 868–878. IEEE (2020)

    Google Scholar 

  26. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)

    Article  MathSciNet  Google Scholar 

  27. Grassal, P.W., Prinzler, M., Leistner, T., Rother, C., Nießner, M., Thies, J.: Neural head avatars from monocular rgb videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18653–18664 (2022)

    Google Scholar 

  28. Gu, J., Liu, L., Wang, P., Theobalt, C.: Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis. arXiv preprint arXiv:2110.08985 (2021)

  29. Guo, X., Kan, M., Chen, T., Shan, S.: Gan with multivariate disentangling for controllable hair editing. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XV. pp. 655–670. Springer (2022)

    Google Scholar 

  30. Guo, Y., Chen, K., Liang, S., Liu, Y.J., Bao, H., Zhang, J.: Ad-nerf: Audio driven neural radiance fields for talking head synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5784–5794 (2021)

    Google Scholar 

  31. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

    Google Scholar 

  32. Hong, Y., Peng, B., Xiao, H., Liu, L., Zhang, J.: Headnerf: A real-time nerf-based parametric head model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20374–20384 (2022)

    Google Scholar 

  33. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4401–4410 (2019)

    Google Scholar 

  34. Kellnhofer, P., Jebe, L.C., Jones, A., Spicer, R., Pulli, K., Wetzstein, G.: Neural lumigraph rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4287–4297 (2021)

    Google Scholar 

  35. Khakhulin, T., Sklyarova, V., Lempitsky, V., Zakharov, E.: Realistic one-shot mesh-based head avatars. In: European Conference on Computer Vision. pp. 345–362. Springer (2022)

    Google Scholar 

  36. Kim, M., Jain, A.K., Liu, X.: Adaface: Quality adaptive margin for face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18750–18759 (2022)

    Google Scholar 

  37. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  38. Kowalski, M., Garbin, S.J., Estellers, V., Baltrušaitis, T., Johnson, M., Shotton, J.: Config: Controllable neural face image generation. In: Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16. pp. 299–315. Springer (2020)

    Google Scholar 

  39. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012)

    Google Scholar 

  40. Lee, C.H., Liu, Z., Wu, L., Luo, P.: Maskgan: Towards diverse and interactive facial image manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5549–5558 (2020)

    Google Scholar 

  41. Lee, Y., Choi, T., Go, H., Lee, H., Cho, S., Kim, J.: Exp-gan: 3d-aware facial image generation with expression control. In: Proceedings of the Asian Conference on Computer Vision. pp. 3812–3827 (2022)

    Google Scholar 

  42. Li, C., Morel-Forster, A., Vetter, T., Egger, B., Kortylewski, A.: Robust model-based face reconstruction through weakly-supervised outlier segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 372–381 (2023)

    Google Scholar 

  43. Li, T., Bolkart, T., Black, M.J., Li, H., Romero, J.: Learning a model of facial shape and expression from 4d scans. ACM Trans. Graph. 36(6), 194–1 (2017)

    Article  Google Scholar 

  44. Loper, M.M., Black, M.J.: Opendr: An approximate differentiable renderer. In: Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part VII 13. pp. 154–169. Springer (2014)

    Google Scholar 

  45. Ma, Z., Zhu, X., Qi, G.J., Lei, Z., Zhang, L.: Otavatar: One-shot talking face avatar with controllable tri-plane rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16901–16910 (2023)

    Google Scholar 

  46. Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: Learning 3d reconstruction in function space. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4460–4470 (2019)

    Google Scholar 

  47. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99–106 (2021)

    Article  Google Scholar 

  48. Mollahosseini, A., Hasani, B., Mahoor, M.H.: Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10(1), 18–31 (2017)

    Article  Google Scholar 

  49. Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., Yang, Y.L.: Hologan: Unsupervised learning of 3d representations from natural images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7588–7597 (2019)

    Google Scholar 

  50. Niemeyer, M., Geiger, A.: Giraffe: Representing scenes as compositional generative neural feature fields. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  51. Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3504–3515 (2020)

    Google Scholar 

  52. Or-El, R., Luo, X., Shan, M., Shechtman, E., Park, J.J., Kemelmacher-Shlizerman, I.: Stylesdf: High-resolution 3d-consistent image and geometry generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13503–13513 (2022)

    Google Scholar 

  53. Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: Learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 165–174 (2019)

    Google Scholar 

  54. Park, K., Sinha, U., Barron, J.T., Bouaziz, S., Goldman, D.B., Seitz, S.M., Martin-Brualla, R.: Nerfies: Deformable neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5865–5874 (2021)

    Google Scholar 

  55. Piao, J., Sun, K., Wang, Q., Lin, K.Y., Li, H.: Inverting generative adversarial renderer for face reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15619–15628 (2021)

    Google Scholar 

  56. Ramamoorthi, R., Hanrahan, P.: An efficient representation for irradiance environment maps. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques. pp. 497–500 (2001)

    Google Scholar 

  57. Ramon, E., Triginer, G., Escur, J., Pumarola, A., Garcia, J., Giro-i Nieto, X., Moreno-Noguer, F.: H3d-net: Few-shot high-fidelity 3d head reconstruction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5620–5629 (2021)

    Google Scholar 

  58. Ranjan, A., Bolkart, T., Sanyal, S., Black, M.J.: Generating 3d faces using convolutional mesh autoencoders. In: Proceedings of the European conference on computer vision (ECCV). pp. 704–720 (2018)

    Google Scholar 

  59. Ravi, N., Reizenstein, J., Novotny, D., Gordon, T., Lo, W.Y., Johnson, J., Gkioxari, G.: Accelerating 3d deep learning with pytorch3d. arXiv preprint arXiv:2007.08501 (2020)

  60. Ren, Y., Li, G., Chen, Y., Li, T.H., Liu, S.: Pirenderer: Controllable portrait image generation via semantic neural rendering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 13759–13768 (2021)

    Google Scholar 

  61. Roich, D., Mokady, R., Bermano, A.H., Cohen-Or, D.: Pivotal tuning for latent-based editing of real images. ACM Transactions on graphics (TOG) 42(1), 1–13 (2022)

    Article  Google Scholar 

  62. Schwarz, K., Liao, Y., Niemeyer, M., Geiger, A.: Graf: Generative radiance fields for 3d-aware image synthesis. Adv. Neural. Inf. Process. Syst. 33, 20154–20166 (2020)

    Google Scholar 

  63. Shoshan, A., Bhonker, N., Kviatkovsky, I., Medioni, G.: Gan-control: Explicitly controllable gans. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14083–14093 (2021)

    Google Scholar 

  64. Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., Sebe, N.: First order motion model for image animation. Advances in neural information processing systems 32 (2019)

    Google Scholar 

  65. Siarohin, A., Menapace, W., Skorokhodov, I., Olszewski, K., Ren, J., Lee, H.Y., Chai, M., Tulyakov, S.: Unsupervised volumetric animation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4658–4669 (2023)

    Google Scholar 

  66. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  67. Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. Adv. Neural. Inf. Process. Syst. 33, 7462–7473 (2020)

    Google Scholar 

  68. Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: Continuous 3d-structure-aware neural scene representations. Advances in Neural Information Processing Systems 32 (2019)

    Google Scholar 

  69. Sun, K., Wu, S., Huang, Z., Zhang, N., Wang, Q., Li, H.: Controllable 3d face synthesis with conditional generative occupancy fields. arXiv preprint arXiv:2206.08361 (2022)

  70. Tewari, A., Elgharib, M., Bernard, F., Seidel, H.P., Pérez, P., Zollhöfer, M., Theobalt, C.: Pie: Portrait image embedding for semantic control. ACM Transactions on Graphics (TOG) 39(6), 1–14 (2020)

    Article  Google Scholar 

  71. Tewari, A., Elgharib, M., Bharaj, G., Bernard, F., Seidel, H.P., Pérez, P., Zollhofer, M., Theobalt, C.: Stylerig: Rigging stylegan for 3d control over portrait images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6142–6151 (2020)

    Google Scholar 

  72. Tewari, A., Pan, X., Fried, O., Agrawala, M., Theobalt, C., et al.: Disentangled3d: Learning a 3d generative model with disentangled geometry and appearance from monocular images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1516–1525 (2022)

    Google Scholar 

  73. Tewari, A., Zollhofer, M., Kim, H., Garrido, P., Bernard, F., Perez, P., Theobalt, C.: Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 1274–1283 (2017)

    Google Scholar 

  74. Tran, L., Liu, X.: Nonlinear 3d face morphable model. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7346–7355 (2018)

    Google Scholar 

  75. Wang, X., Guo, Y., Yang, Z., Zhang, J.: Prior-guided multi-view 3d head reconstruction. IEEE Trans. Multimedia 24, 4028–4040 (2021)

    Article  Google Scholar 

  76. Wu, S., Yan, Y., Li, Y., Cheng, Y., Zhu, W., Gao, K., Li, X., Zhai, G.: Ganhead: Towards generative animatable neural head avatars. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 437–447 (2023)

    Google Scholar 

  77. Wu, Y., Deng, Y., Yang, J., Wei, F., Chen, Q., Tong, X.: Anifacegan: Animatable 3d-aware face image generation for video avatars. arXiv preprint arXiv:2210.06465 (2022)

  78. Xu, S., Yang, J., Chen, D., Wen, F., Deng, Y., Jia, Y., Tong, X.: Deep 3d portrait from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7710–7720 (2020)

    Google Scholar 

  79. Yao, S., Zhong, R., Yan, Y., Zhai, G., Yang, X.: Dfa-nerf: Personalized talking head generation via disentangled face attributes neural rendering. arXiv preprint arXiv:2201.00791 (2022)

  80. Yenamandra, T., Tewari, A., Bernard, F., Seidel, H., Elgharib, M., Cremers, D., Theobalt, C.: i3dmm: Deep implicit 3d morphable model of human heads. In: Proceedings of the IEEE / CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2021)

    Google Scholar 

  81. Yenamandra, T., Tewari, A., Bernard, F., Seidel, H.P., Elgharib, M., Cremers, D., Theobalt, C.: i3dmm: Deep implicit 3d morphable model of human heads. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12803–12813 (2021)

    Google Scholar 

  82. Yin, Y., Ghasedi, K., Wu, H., Yang, J., Tong, X., Fu, Y.: Nerfinvertor: High fidelity nerf-gan inversion for single-shot real image animation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8539–8548 (2023)

    Google Scholar 

  83. Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European conference on computer vision (ECCV). pp. 325–341 (2018)

    Google Scholar 

  84. Yue, Z., Loy, C.C.: Difface: Blind face restoration with diffused error contraction. arXiv preprint arXiv:2212.06512 (2022)

  85. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 586–595 (2018)

    Google Scholar 

  86. Zheng, M., Yang, H., Huang, D., Chen, L.: Imface: A nonlinear 3d morphable face model with implicit neural representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20343–20352 (2022)

    Google Scholar 

  87. Zheng, Y., Abrevaya, V.F., Bühler, M.C., Chen, X., Black, M.J., Hilliges, O.: Im avatar: Implicit morphable head avatars from videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13545–13555 (2022)

    Google Scholar 

  88. Zheng, Y., Yifan, W., Wetzstein, G., Black, M.J., Hilliges, O.: Pointavatar: Deformable point-based head avatars from videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21057–21067 (2023)

    Google Scholar 

  89. Zhuang, Y., Zhu, H., Sun, X., Cao, X.: Mofanerf: Morphable facial neural radiance field. In: European Conference on Computer Vision (2022)

    Google Scholar 

  90. Zielonka, W., Bolkart, T., Thies, J.: Instant volumetric head avatars. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4574–4584 (2023)

    Google Scholar 

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  1. University of Science and Technology of China, Hefei, Anhui, China

    Dingyun Zhang & Juyong Zhang

  2. The Chinese University of Hong Kong, Shenzhen, Guangdong, China

    Heyuan Li

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  1. Dingyun Zhang
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  2. Heyuan Li
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  1. Victoria University of Wellington, Wellington, New Zealand

    Fang-Lue Zhang

  2. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

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Zhang, D., Li, H., Zhang, J. (2024). Zero-Shot Real Facial Attribute Separation and Transfer at Novel Views. In: Zhang, FL., Sharf, A. (eds) Computational Visual Media. CVM 2024. Lecture Notes in Computer Science, vol 14593. Springer, Singapore. https://doi.org/10.1007/978-981-97-2092-7_1

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