“Neural scene graph rendering”
Conference:
Type(s):
Title:
- Neural scene graph rendering
Session/Category Title: Summary and Q&A: Geometry Learning
Presenter(s)/Author(s):
Abstract:
We present a neural scene graph—a modular and controllable representation of scenes with elements that are learned from data. We focus on the forward rendering problem, where the scene graph is provided by the user and references learned elements. The elements correspond to geometry and material definitions of scene objects and constitute the leaves of the graph; we store them as high-dimensional vectors. The position and appearance of scene objects can be adjusted in an artist-friendly manner via familiar transformations, e.g. translation, bending, or color hue shift, which are stored in the inner nodes of the graph. In order to apply a (non-linear) transformation to a learned vector, we adopt the concept of linearizing a problem by lifting it into higher dimensions: we first encode the transformation into a high-dimensional matrix and then apply it by standard matrix-vector multiplication. The transformations are encoded using neural networks. We render the scene graph using a streaming neural renderer, which can handle graphs with a varying number of objects, and thereby facilitates scalability. Our results demonstrate a precise control over the learned object representations in a number of animated 2D and 3D scenes. Despite the limited visual complexity, our work presents a step towards marrying traditional editing mechanisms with learned representations, and towards high-quality, controllable neural rendering.
References:
1. Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. 2020. Neural Point-Based Graphics. In Computer Vision – ECCV 2020, Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Springer International Publishing, Cham, 696–712.Google ScholarDigital Library
2. Oron Ashual and Lior Wolf. 2019. Specifying Object Attributes and Relations in Interactive Scene Generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).Google ScholarCross Ref
3. Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009. Curriculum Learning. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML ’09). Association for Computing Machinery, New York, NY, USA, 41–48. Google ScholarDigital Library
4. Brent Burley. 2012. Physically based shading at Disney. In ACM SIGGRAPH Courses: Practical Physically-Based Shading in Film and Game Production. ACM, New York, NY, USA, 18:35–18:48. Google ScholarDigital Library
5. Xuelin Chen, Daniel Cohen-Or, Baoquan Chen, and Niloy J. Mitra. 2021. Towards a Neural Graphics Pipeline for Controllable Image Generation. Computer Graphics Forum 40, 2 (2021).Google Scholar
6. Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. In Advances in Neural Information Processing Systems 29. Curran Associates, Inc., 2172–2180.Google Scholar
7. Corinna Cortes and Vladimir Vapnik. 1995. Support-Vector Networks. Mach. Learn. 20, 3 (Sept. 1995), 273–297. Google ScholarDigital Library
8. Sebastien Ehrhardt, Oliver Groth, Aron Monszpart, Martin Engelcke, Ingmar Posner, Niloy Mitra, and Andrea Vedaldi. 2020. RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 11202–11213. https://proceedings.neurips.cc/paper/2020/file/806beafe154032a5b818e97b4420ad98-Paper.pdfGoogle Scholar
9. S. M. Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman, Andrei A. Rusu, Ivo Danihelka, Karol Gregor, David P. Reichert, Lars Buesing, Theophane Weber, Oriol Vinyals, Dan Rosenbaum, Neil Rabinowitz, Helen King, Chloe Hillier, Matt Botvinick, Daan Wierstra, Koray Kavukcuoglu, and Demis Hassabis. 2018. Neural Scene Representation and Rendering. Science 360, 6394 (2018), 1204–1210. Google ScholarCross Ref
10. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger (Eds.), Vol. 27. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdfGoogle Scholar
11. Jonathan Granskog, Fabrice Rousselle, Marios Papas, and Jan Novák. 2020. Compositional Neural Scene Representations for Shading Inference. ACM Transactions on Graphics (Proceedings of SIGGRAPH) 39, 4 (July 2020).Google Scholar
12. Michelle Guo, Alireza Fathi, Jiajun Wu, and Thomas Funkhouser. 2020. Object-Centric Neural Scene Rendering. arXiv preprint arXiv:2012.08503 (2020).Google Scholar
13. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV) (ICCV ’15). IEEE Computer Society, USA, 1026–1034. Google ScholarDigital Library
14. Pedro Hermosilla, Sebastian Maisch, Tobias Ritschel, and Timo Ropinski. 2019. Deep-learning the Latent Space of Light Transport. Computer Graphics Forum 38, 4 (2019), 207–217. Google ScholarCross Ref
15. Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, and Amir Globerson. 2020. Learning Canonical Representations for Scene Graph to Image Generation. In European Conference on Computer Vision.Google ScholarDigital Library
16. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735–1780. arXiv:https://doi.org/10.1162/neco.1997.9.8.1735 Google ScholarDigital Library
17. Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris. 2020. GANSpace: Discovering Interpretable GAN Controls. In Proc. NeurIPS.Google Scholar
18. Maor Ivgi, Yaniv Benny, Avichai Ben-David, Jonathan Berant, and Lior Wolf. 2020. Scene Graph to Image Generation with Contextualized Object Layout Refinement. arXiv:cs.CV/2009.10939Google Scholar
19. Justin Johnson, Agrim Gupta, and Li Fei-Fei. 2018. Image Generation from Scene Graphs. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1219–1228. Google ScholarCross Ref
20. Tero Karras, Samuli Laine, and Timo Aila. 2019. A Style-Based Generator Architecture for Generative Adversarial Networks. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 4396–4405. Google ScholarCross Ref
21. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020. Analyzing and Improving the Image Quality of StyleGAN. In Proc. CVPR.Google ScholarCross Ref
22. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980Google Scholar
23. Tejas D Kulkarni, William F. Whitney, Pushmeet Kohli, and Josh Tenenbaum. 2015. Deep Convolutional Inverse Graphics Network. In Advances in Neural Information Processing Systems 28. Curran Associates, Inc., 2539–2547.Google Scholar
24. Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang. 2020. Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes. ArXiv abs/2011.13084 (2020).Google Scholar
25. Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. 2020. Neural Sparse Voxel Fields. NeurIPS (2020).Google Scholar
26. Yunchao Liu, Jiajun Wu, Zheng Wu, Daniel Ritchie, William T. Freeman, and Joshua B. Tenenbaum. 2019. Learning to Describe Scenes with Programs. In International Conference on Learning Representations. https://openreview.net/forum?id=SyNPk2R9K7Google Scholar
27. Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. 2019. Neural Volumes: Learning Dynamic Renderable Volumes from Images. ACM Trans. Graph. 38, 4, Article Article 65 (July 2019), 14 pages. Google ScholarDigital Library
28. Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019. Occupancy Networks: Learning 3D Reconstruction in Function Space. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
29. Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. 2020. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. In ECCV.Google Scholar
30. Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang. 2019. HoloGAN: Unsupervised Learning of 3D Representations From Natural Images. In The IEEE International Conference on Computer Vision (ICCV).Google Scholar
31. Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, and Niloy Mitra. 2020. BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images. In Advances in Neural Information Processing Systems 33.Google Scholar
32. Thu H Nguyen-Phuoc, Chuan Li, Stephen Balaban, and Yongliang Yang. 2018. Render-Net: A Deep Convolutional Network for Differentiable Rendering from 3D Shapes. In Advances in Neural Information Processing Systems 31. Curran Associates, Inc., 7891–7901.Google Scholar
33. Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debhath, Anjul Patney, Ankit B. Patel, and Anima Anandkumar. 2020. Semi-Supervised StyleGAN for Disentanglement Learning. arXiv:cs.CV/2003.03461Google Scholar
34. Michael Niemeyer and Andreas Geiger. 2021. GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields. In Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
35. Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. 2020. Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
36. Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger. 2019. Texture Fields: Learning Texture Representations in Function Space. In International Conference on Computer Vision.Google Scholar
37. Michael Oechsle, Michael Niemeyer, Christian Reiser, Lars Mescheder, Thilo Strauss, and Andreas Geiger. 2020. Learning Implicit Surface Light Fields. In International Conference on 3D Vision (3DV).Google Scholar
38. Kyle Olszewski, Sergey Tulyakov, Oliver Woodford, Hao Li, and Linjie Luo. 2019. Transformable Bottleneck Networks. The IEEE International Conference on Computer Vision (ICCV) (Nov 2019).Google Scholar
39. Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide. 2020. Neural Scene Graphs for Dynamic Scenes.Google Scholar
40. Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. 2019. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
41. Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, and Ricardo Martin-Brualla. 2020. Deformable Neural Radiance Fields. arXiv preprint arXiv:2011.12948 (2020).Google Scholar
42. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 8024–8035. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdfGoogle Scholar
43. Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. 2020. D-NeRF: Neural Radiance Fields for Dynamic Scenes. arXiv preprint arXiv:2011.13961 (2020).Google Scholar
44. Konstantinos Rematas and Vittorio Ferrari. 2020. Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool. In CVPR.Google Scholar
45. Paul Sanzenbacher, Lars Mescheder, and Andreas Geiger. 2020. Learning Neural Light Transport. arXiv:cs.CV/2006.03427Google Scholar
46. Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1409.1556Google Scholar
47. Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. 2019. Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 1119–1130.Google Scholar
48. Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng. 2020. Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains. NeurIPS (2020).Google Scholar
49. Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox. 2016. Multi-view 3D Models from Single Images with a Convolutional Network. In Computer Vision – ECCV 2016. Springer International Publishing, Cham, 322–337.Google ScholarCross Ref
50. Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B Goldman, and Michael Zollhöfer. 2020. State of the Art on Neural Rendering. Computer Graphics Forum 39, 2 (2020), 701–727. Google ScholarCross Ref
51. Justus Thies, Michael Zollhöfer, and Matthias Nießner. 2019. Deferred Neural Rendering: Image Synthesis Using Neural Textures. ACM Trans. Graph. 38, 4, Article Article 66 (July 2019), 12 pages. Google ScholarDigital Library
52. Wenqi Xian, Jia-Bin Huang, Johannes Kopf, and Changil Kim. 2020. Space-time Neural Irradiance Fields for Free-Viewpoint Video. arXiv preprint arXiv:2011.12950 (2020).Google Scholar
53. Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref
