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Generating Videos of Zero-Shot Compositions of Actions and Objects

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12357)

Abstract

Human activity videos involve rich, varied interactions between people and objects. In this paper we develop methods for generating such videos – making progress toward addressing the important, open problem of video generation in complex scenes. In particular, we introduce the task of generating human-object interaction videos in a zero-shot compositional setting, i.e., generating videos for action-object compositions that are unseen during training, having seen the target action and target object separately. This setting is particularly important for generalization in human activity video generation, obviating the need to observe every possible action-object combination in training and thus avoiding the combinatorial explosion involved in modeling complex scenes. To generate human-object interaction videos, we propose a novel adversarial framework HOI-GAN which includes multiple discriminators focusing on different aspects of a video. To demonstrate the effectiveness of our proposed framework, we perform extensive quantitative and qualitative evaluation on two challenging datasets: EPIC-Kitchens and 20BN-Something-Something v2.

Keywords

Video generation Compositionality in videos 

Notes

Acknowledgements

This work was done when Megha Nawhal was an intern at Borealis AI. We would like to thank the Borealis AI team for participating in our user study.

Supplementary material

504453_1_En_23_MOESM1_ESM.zip (53 mb)
Supplementary material 1 (zip 54282 KB)

References

  1. 1.
    Akata, Z., Reed, S., Walter, D., Lee, H., Schiele, B.: Evaluation of output embeddings for fine-grained image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)Google Scholar
  2. 2.
    Anderson, P., Fernando, B., Johnson, M., Gould, S.: SPICE: semantic propositional image caption evaluation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 382–398. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46454-1_24CrossRefGoogle Scholar
  3. 3.
    Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning (ICML) (2017)Google Scholar
  4. 4.
    Bansal, A., Ma, S., Ramanan, D., Sheikh, Y.: Recycle-GAN: unsupervised video retargeting. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 122–138. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01228-1_8CrossRefGoogle Scholar
  5. 5.
    Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: International Conference on Learning Representations (ICLR) (2019)Google Scholar
  6. 6.
    Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  7. 7.
    Chao, Y.W., Liu, Y., Liu, X., Zeng, H., Deng, J.: Learning to detect human-object interactions. In: IEEE Winter Conference on Applications of Computer Vision (WACV) (2018)Google Scholar
  8. 8.
    Chao, Y.W., Wang, Z., He, Y., Wang, J., Deng, J.: HICO: a benchmark for recognizing human-object interactions in images. In: IEEE International Conference on Computer Vision (ICCV) (2015)Google Scholar
  9. 9.
    Damen, D., et al.: Scaling egocentric vision: the EPIC-KITCHENS dataset. In: European Conference on Computer Vision (ECCV) (2018)Google Scholar
  10. 10.
    Delaitre, V., Fouhey, D.F., Laptev, I., Sivic, J., Gupta, A., Efros, A.A.: Scene semantics from long-term observation of people. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 284–298. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33783-3_21CrossRefGoogle Scholar
  11. 11.
    Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems (NIPS) (2015)Google Scholar
  12. 12.
    Denton, E.L., et al.: Unsupervised learning of disentangled representations from video. In: Advances in Neural Information Processing Systems (NIPS) (2017)Google Scholar
  13. 13.
    Desai, C., Ramanan, D.: Detecting actions, poses, and objects with relational phraselets. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 158–172. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33765-9_12CrossRefGoogle Scholar
  14. 14.
    Ebdelli, M., Le Meur, O., Guillemot, C.: Video inpainting with short-term windows: application to object removal and error concealment. IEEE Trans. Image Process. 24(10), 3034–3047 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Elhoseiny, M., Saleh, B., Elgammal, A.: Write a classifier: zero-shot learning using purely textual descriptions. In: IEEE International Conference on Computer Vision (ICCV) (2013)Google Scholar
  16. 16.
    Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)Google Scholar
  17. 17.
    Fouhey, D.F., Delaitre, V., Gupta, A., Efros, A.A., Laptev, I., Sivic, J.: People watching: human actions as a cue for single view geometry. Int. J. Comput. Vis. (IJCV) 110, 259–274 (2014).  https://doi.org/10.1007/s11263-014-0710-zCrossRefGoogle Scholar
  18. 18.
    Gkioxari, G., Girshick, R., Dollár, P., He, K.: Detecting and recognizing human-object interactions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  19. 19.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NIPS) (2014)Google Scholar
  20. 20.
    Goyal, R., et al.: The “something something” video database for learning and evaluating visual common sense. In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  21. 21.
    Grabner, H., Gall, J., Van Gool, L.: What makes a chair a chair? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)Google Scholar
  22. 22.
    Granados, M., Kim, K.I., Tompkin, J., Kautz, J., Theobalt, C.: Background inpainting for videos with dynamic objects and a free-moving camera. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 682–695. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33718-5_49CrossRefGoogle Scholar
  23. 23.
    Guadarrama, S., et al.: YouTube2Text: recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition. In: IEEE International Conference on Computer Vision (ICCV) (2013)Google Scholar
  24. 24.
    Gupta, A., Davis, L.S.: Objects in action: an approach for combining action understanding and object perception. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007)Google Scholar
  25. 25.
    He, J., Lehrmann, A., Marino, J., Mori, G., Sigal, L.: Probabilistic video generation using holistic attribute control. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 466–483. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01228-1_28CrossRefGoogle Scholar
  26. 26.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  27. 27.
    Hsieh, J.T., Liu, B., Huang, D.A., Fei-Fei, L.F., Niebles, J.C.: Learning to decompose and disentangle representations for video prediction. In: Advances in Neural Information Processing Systems (NIPS) (2018)Google Scholar
  28. 28.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML) (2015)Google Scholar
  29. 29.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  30. 30.
    Johnson, J., Gupta, A., Fei-Fei, L.: Image generation from scene graphs. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  31. 31.
    Johnson, J., et al.: Image retrieval using scene graphs. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)Google Scholar
  32. 32.
    Kalchbrenner, N., et al.: Video pixel networks. In: International Conference on Machine Learning (ICML) (2017)Google Scholar
  33. 33.
    Kalogeiton, V., Weinzaepfel, P., Ferrari, V., Schmid, C.: Joint learning of object and action detectors. In: IEEE International Conference on Computer Vision (ICCV). IEEE (2017)Google Scholar
  34. 34.
    Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: International Conference on Learning Representations (ICLR) (2018)Google Scholar
  35. 35.
    Kato, K., Li, Y., Gupta, A.: Compositional learning for human object interaction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 247–264. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01264-9_15CrossRefGoogle Scholar
  36. 36.
    Kim, T., Cha, M., Kim, H., Lee, J.K., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks (2017)Google Scholar
  37. 37.
    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)Google Scholar
  38. 38.
    Kjellström, H., Romero, J., Kragić, D.: Visual object-action recognition: Inferring object affordances from human demonstration. Comput. Vis. Image Underst. (CVIU) 115(1), 81–90 (2011)CrossRefGoogle Scholar
  39. 39.
    Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. (IJCV) 123, 32–73 (2017).  https://doi.org/10.1007/s11263-016-0981-7MathSciNetCrossRefGoogle Scholar
  40. 40.
    Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)Google Scholar
  41. 41.
    Lei Ba, J., Swersky, K., Fidler, S., et al.: Predicting deep zero-shot convolutional neural networks using textual descriptions. In: IEEE International Conference on Computer Vision (2015)Google Scholar
  42. 42.
    Liang, X., Lee, L., Dai, W., Xing, E.P.: Dual motion GAN for future-flow embedded video prediction. In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  43. 43.
    Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems (NIPS) (2017)Google Scholar
  44. 44.
    Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 852–869. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_51CrossRefGoogle Scholar
  45. 45.
    Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: International Conference on Machine Learning (ICML) (2013)Google Scholar
  46. 46.
    Mathieu, M., Couprie, C., LeCun, Y.: Deep multi-scale video prediction beyond mean square error. In: International Conference on Learning Representations (ICLR) (2016)Google Scholar
  47. 47.
    Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (ICLR) (2018)Google Scholar
  48. 48.
    Miyato, T., Koyama, M.: cGANs with projection discriminator. In: International Conference on Learning Representations (ICLR) (2018)Google Scholar
  49. 49.
    Newell, A., Deng, J.: Pixels to graphs by associative embedding. In: Advances in Neural Information Processing Systems (NeurIPS) (2017)Google Scholar
  50. 50.
    Newson, A., Almansa, A., Fradet, M., Gousseau, Y., Pérez, P.: Video inpainting of complex scenes. SIAM J. Imaging Sci. 7(4), 1993–2019 (2014)MathSciNetCrossRefGoogle Scholar
  51. 51.
    Niklaus, S., Mai, L., Liu, F.: Video frame interpolation via adaptive separable convolution. In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  52. 52.
    Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: International Conference on Machine Learning (ICML) (2017)Google Scholar
  53. 53.
    van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with PixelCNN decoders. In: Advances in Neural Information Processing Systems (NIPS) (2016)Google Scholar
  54. 54.
    Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)Google Scholar
  55. 55.
    Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text-to-image synthesis. In: International Conference on Machine Learning (ICML) (2016)Google Scholar
  56. 56.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015).  https://doi.org/10.1007/s11263-015-0816-yMathSciNetCrossRefGoogle Scholar
  57. 57.
    Saito, M., Matsumoto, E., Saito, S.: Temporal generative adversarial nets with singular value clipping. In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  58. 58.
    Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems (NIPS) (2016)Google Scholar
  59. 59.
    Shen, Y., Lu, F., Cao, X., Foroosh, H.: Video completion for perspective camera under constrained motion. In: International Conference on Pattern Recognition (ICPR) (2006)Google Scholar
  60. 60.
    Sigurdsson, G.A., Russakovsky, O., Gupta, A.: What actions are needed for understanding human actions in videos? In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  61. 61.
    Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using LSTMs. In: International Conference on Machine Learning (ICML) (2015)Google Scholar
  62. 62.
    Stark, L., Bowyer, K.: Achieving generalized object recognition through reasoning about association of function to structure. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 13(10), 1097–1104 (1991)CrossRefGoogle Scholar
  63. 63.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  64. 64.
    Tulyakov, S., Liu, M.Y., Yang, X., Kautz, J.: MoCoGAN: decomposing motion and content for video generation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  65. 65.
    Villegas, R., Yang, J., Hong, S., Lin, X., Lee, H.: Decomposing motion and content for natural video sequence prediction. In: International Conference on Learning Representations (ICLR) (2017)Google Scholar
  66. 66.
    Vondrick, C., Pirsiavash, H., Torralba, A.: Generating videos with scene dynamics. In: Advances in Neural Information Processing Systems (NIPS) (2016)Google Scholar
  67. 67.
    Walker, J., Doersch, C., Gupta, A., Hebert, M.: An uncertain future: forecasting from static images using variational autoencoders. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 835–851. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46478-7_51CrossRefGoogle Scholar
  68. 68.
    Walker, J., Marino, K., Gupta, A., Hebert, M.: The pose knows: video forecasting by generating pose futures. In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  69. 69.
    Wang, T.C., Liu, M.Y., Tao, A., Liu, G., Kautz, J., Catanzaro, B.: Few-shot video-to-video synthesis. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)Google Scholar
  70. 70.
    Wang, T.C., et al.: Video-to-video synthesis. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)Google Scholar
  71. 71.
    Xian, Y., Lorenz, T., Schiele, B., Akata, Z.: Feature generating networks for zero-shot learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  72. 72.
    Xian, Y., Schiele, B., Akata, Z.: Zero-shot learning-the good, the bad and the ugly. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  73. 73.
    Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  74. 74.
    Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  75. 75.
    Xu, T., et al.: AttnGAN: fine-grained text to image generation with attentional generative adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  76. 76.
    Yao, B., Fei-Fei, L.: Modeling mutual context of object and human pose in human-object interaction activities. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)Google Scholar
  77. 77.
    Zellers, R., Yatskar, M., Thomson, S., Choi, Y.: Neural motifs: scene graph parsing with global context. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  78. 78.
    Zhang, H., et al.: StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  79. 79.
    Zhao, J., Mathieu, M., LeCun, Y.: Energy-based generative adversarial network. In: International Conference on Learning Representations (ICLR) (2017)Google Scholar
  80. 80.
    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  81. 81.
    Zhu, Y., Elhoseiny, M., Liu, B., Peng, X., Elgammal, A.: A generative adversarial approach for zero-shot learning from noisy texts. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Borealis AIVancouverCanada
  2. 2.Simon Fraser UniversityBurnabyCanada
  3. 3.University of British ColumbiaVancouverCanada
  4. 4.Vector Institute for AITorontoCanada
  5. 5.CIFAR AI ChairTorontoCanada

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