Skip to main content
Springer Nature Link
Log in

Motion Transformer for Unsupervised Image Animation

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13676))

Included in the following conference series:

Abstract

Image animation aims to animate a source image by using motion learned from a driving video. Current state-of-the-art methods typically use convolutional neural networks (CNNs) to predict motion information, such as motion keypoints and corresponding local transformations. However, these CNN based methods do not explicitly model the interactions between motions; as a result, the important underlying motion relationship may be neglected, which can potentially lead to noticeable artifacts being produced in the generated animation video. To this end, we propose a new method, the motion transformer, which is the first attempt to build a motion estimator based on a vision transformer. More specifically, we introduce two types of tokens in our proposed method: i) image tokens formed from patch features and corresponding position encoding; and ii) motion tokens encoded with motion information. Both types of tokens are sent into vision transformers to promote underlying interactions between them through multi-head self attention blocks. By adopting this process, the motion information can be better learned to boost the model performance. The final embedded motion tokens are then used to predict the corresponding motion keypoints and local transformations. Extensive experiments on benchmark datasets show that our proposed method achieves promising results to the state-of-the-art baselines. Our source code will be public available.

J. Tao—Work done during an intership at Alibaba Group.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Balakrishnan, G., Zhao, A., Dalca, A.V., Durand, F., Guttag, J.: Synthesizing images of humans in unseen poses. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8340–8348 (2018)

    Google Scholar 

  2. Bhatnagar, B.L., Tiwari, G., Theobalt, C., Pons-Moll, G.: Multi-garment net: learning to dress 3D people from images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5420–5430 (2019)

    Google Scholar 

  3. Burkov, E., Pasechnik, I., Grigorev, A., Lempitsky, V.: Neural head reenactment with latent pose descriptors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13786–13795 (2020)

    Google Scholar 

  4. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

    Chapter  Google Scholar 

  5. Chan, C., Ginosar, S., Zhou, T., Efros, A.A.: Everybody dance now. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5933–5942 (2019)

    Google Scholar 

  6. Chen, X., Song, J., Hilliges, O.: Unpaired pose guided human image generation. In: Conference on Computer Vision and Pattern Recognition (CVPR 2019). Computer Vision Foundation (CVF) (2019)

    Google Scholar 

  7. Chopra, A., Jain, R., Hemani, M., Krishnamurthy, B.: Zflow: gated appearance flow-based virtual try-on with 3d priors. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5433–5442 (2021)

    Google Scholar 

  8. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  9. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021)

    Google Scholar 

  10. Gao, Q., Wang, B., Liu, L., Chen, B.: Unsupervised co-part segmentation through assembly. In: International Conference on Machine Learning (2021)

    Google Scholar 

  11. Geng, Z., Cao, C., Tulyakov, S.: 3D guided fine-grained face manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9821–9830 (2019)

    Google Scholar 

  12. Ha, S., Kersner, M., Kim, B., Seo, S., Kim, D.: Marionette: few-shot face reenactment preserving identity of unseen targets. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 10893–10900 (2020)

    Google Scholar 

  13. Huang, Z., Han, X., Xu, J., Zhang, T.: Few-shot human motion transfer by personalized geometry and texture modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2297–2306 (2021)

    Google Scholar 

  14. Jiang, Y., Yang, S., Qiu, H., Wu, W., Loy, C.C., Liu, Z.: Text2human: text-driven controllable human image generation. ACM Trans. Graphics (TOG) 41(4), 1–11 (2022). https://doi.org/10.1145/3528223.3530104

    Article  Google Scholar 

  15. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

  16. Kim, H., et al.: Deep video portraits. ACM Trans. Graphics (TOG) 37(4), 163 (2018)

    Article  Google Scholar 

  17. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)

    Google Scholar 

  18. Li, Y., et al.: Tokenpose: learning keypoint tokens for human pose estimation. In: IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  19. Li, Y., Huang, C., Loy, C.C.: Dense intrinsic appearance flow for human pose transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3693–3702 (2019)

    Google Scholar 

  20. Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., Wu, F.: Diverse part discovery: occluded person re-identification with part-aware transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898–2907 (2021)

    Google Scholar 

  21. Liu, W., Piao, Z., Min, J., Luo, W., Ma, L., Gao, S.: Liquid warping GAN: a unified framework for human motion imitation, appearance transfer and novel view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5904–5913 (2019)

    Google Scholar 

  22. Lorenz, D., Bereska, L., Milbich, T., Ommer, B.: Unsupervised part-based disentangling of object shape and appearance. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10955–10964 (2019)

    Google Scholar 

  23. Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., Van Gool, L.: Pose guided person image generation. In: Advances in Neural Information Processing Systems, vol. 30, pp. 406–416 (2017)

    Google Scholar 

  24. Ma, L., Sun, Q., Georgoulis, S., Van Gool, L., Schiele, B., Fritz, M.: Disentangled person image generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 99–108 (2018)

    Google Scholar 

  25. Nagrani, A., Chung, J.S., Zisserman, A.: Voxceleb: a large-scale speaker identification dataset. arXiv preprint arXiv:1706.08612 (2017)

  26. Neverova, N., Alp Güler, R., Kokkinos, I.: Dense pose transfer. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 128–143. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_8

    Chapter  Google Scholar 

  27. Nirkin, Y., Keller, Y., Hassner, T.: FSGAN: subject agnostic face swapping and reenactment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7184–7193 (2019)

    Google Scholar 

  28. Pumarola, A., Agudo, A., Martinez, A.M., Sanfeliu, A., Moreno-Noguer, F.: GANimation: anatomically-aware facial animation from a single image. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 835–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_50

    Chapter  Google Scholar 

  29. Ren, J., Chai, M., Tulyakov, S., Fang, C., Shen, X., Yang, J.: Human motion transfer from poses in the wild. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12537, pp. 262–279. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-67070-2_16

    Chapter  Google Scholar 

  30. Ren, J., Chai, M., Woodford, O.J., Olszewski, K., Tulyakov, S.: Flow guided transformable bottleneck networks for motion retargeting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10795–10805 (2021)

    Google Scholar 

  31. Ren, Y., Yu, X., Chen, J., Li, T.H., Li, G.: Deep image spatial transformation for person image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7690–7699 (2020)

    Google Scholar 

  32. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  33. Sarkar, K., Mehta, D., Xu, W., Golyanik, V., Theobalt, C.: Neural re-rendering of humans from a single image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 596–613. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_35

    Chapter  Google Scholar 

  34. Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., Sebe, N.: Animating arbitrary objects via deep motion transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2377–2386 (2019)

    Google Scholar 

  35. Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., Sebe, N.: First order motion model for image animation. In: Advances in Neural Information Processing Systems (2019)

    Google Scholar 

  36. Siarohin, A., Sangineto, E., Lathuiliere, S., Sebe, N.: Deformable GANs for pose-based human image generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3408–3416 (2018)

    Google Scholar 

  37. Siarohin, A., Woodford, O., Ren, J., Chai, M., Tulyakov, S.: Motion representations for articulated animation. In: CVPR (2021)

    Google Scholar 

  38. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)

    Google Scholar 

  39. Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693–5703 (2019)

    Google Scholar 

  40. Tao, J., et al.: Structure-aware motion transfer with deformable anchor model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3637–3646 (2022)

    Google Scholar 

  41. Tripathy, S., Kannala, J., Rahtu, E.: Facegan: Facial attribute controllable reenactment gan. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1329–1338 (2021)

    Google Scholar 

  42. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  43. Wan, Z., Zhang, J., Chen, D., Liao, J.: High-fidelity pluralistic image completion with transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 4692–4701, October 2021

    Google Scholar 

  44. Wang, T.C., Mallya, A., Liu, M.Y.: One-shot free-view neural talking-head synthesis for video conferencing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10039–10049 (2021)

    Google Scholar 

  45. Watchareeruetai, U., et al.: Lotr: face landmark localization using localization transformer. arXiv preprint arXiv:2109.10057 (2021)

  46. Wei, D., Xu, X., Shen, H., Huang, K.: C2f-FWN: coarse-to-fine flow warping network for spatial-temporal consistent motion transfer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 4, 2852–2860, May 2021. https://ojs.aaai.org/index.php/AAAI/article/view/16391

  47. Wei, Y., Liu, M., Wang, H., Zhu, R., Hu, G., Zuo, W.: Learning flow-based feature warping for face frontalization with illumination inconsistent supervision. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 558–574. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_33

    Chapter  Google Scholar 

  48. Wiles, O., Koepke, A.S., Zisserman, A.: X2Face: a network for controlling face generation using images, audio, and pose codes. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 690–706. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_41

    Chapter  Google Scholar 

  49. Xu, B., et al.: Move as you like: image animation in e-commerce scenario. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 2759–2761 (2021)

    Google Scholar 

  50. Yang, S., Quan, Z., Nie, M., Yang, W.: Transpose: keypoint localization via transformer. In: IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  51. Yao, G., et al.: One-shot face reenactment using appearance adaptive normalization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3172–3180 (2021)

    Google Scholar 

  52. Yoon, J.S., Liu, L., Golyanik, V., Sarkar, K., Park, H.S., Theobalt, C.: Pose-guided human animation from a single image in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15039–15048, June 2021

    Google Scholar 

  53. Yu, X., Rao, Y., Wang, Z., Liu, Z., Lu, J., Zhou, J.: Pointr: diverse point cloud completion with geometry-aware transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12498–12507 (2021)

    Google Scholar 

  54. Zablotskaia, P., Siarohin, A., Zhao, B., Sigal, L.: DWNET: dense warp-based network for pose-guided human video generation. In: BMVC, p. 51 (2019)

    Google Scholar 

  55. Zakharov, E., Shysheya, A., Burkov, E., Lempitsky, V.: Few-shot adversarial learning of realistic neural talking head models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9459–9468 (2019)

    Google Scholar 

  56. Zhang, J., Li, K., Lai, Y.K., Yang, J.: PISE: person image synthesis and editing with decoupled GAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7982–7990 (2021)

    Google Scholar 

  57. Zhao, L., Peng, X., Tian, Yu., Kapadia, M., Metaxas, D.: Learning to forecast and refine residual motion for image-to-video generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 403–419. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_24

    Chapter  Google Scholar 

  58. Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881–6890 (2021)

    Google Scholar 

  59. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable {detr}: Deformable transformers for end-to-end object detection. In: International Conference on Learning Representations (2021)

    Google Scholar 

  60. Zhu, Z., Huang, T., Shi, B., Yu, M., Wang, B., Bai, X.: Progressive pose attention transfer for person image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2347–2356 (2019)

    Google Scholar 

Download references

Acknowledgement

This work is supported by the Major Project for New Generation of AI under Grant No. 2018AAA0100400, the National Natural Science Foundation of China (Grant No. 62176047), Sichuan Science and Technology Program (No. 2021YFS0374, 2022YFS0600), Beijing Natural Science Foundation (Z190023), and Alibaba Group through Alibaba Innovation Research Program. This work is also partially supported by the Science and Technology on Electronic Information Control Laboratory.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering and Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu, China

    Jiale Tao, Wen Li & Lixin Duan

  2. Alibaba Group, Hangzhou, China

    Biao Wang, Tiezheng Ge & Yuning Jiang

Authors
  1. Jiale Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Biao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tiezheng Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuning Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lixin Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wen Li .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 2330 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tao, J., Wang, B., Ge, T., Jiang, Y., Li, W., Duan, L. (2022). Motion Transformer for Unsupervised Image Animation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13676. Springer, Cham. https://doi.org/10.1007/978-3-031-19787-1_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19787-1_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19786-4

  • Online ISBN: 978-3-031-19787-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics