article

A Database and Evaluation Methodology for Optical Flow

Authors:

Daniel Scharstein,

Michael J. Black,

Richard SzeliskiAuthors Info & Claims

International Journal of Computer Vision, Volume 92, Issue 1

Pages 1 - 31

https://doi.org/10.1007/s11263-010-0390-2

Published: 01 March 2011 Publication History

Abstract

The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. The challenges for optical flow algorithms today go beyond the datasets and evaluation methods proposed in that paper. Instead, they center on problems associated with complex natural scenes, including nonrigid motion, real sensor noise, and motion discontinuities. We propose a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms. To that end, we contribute four types of data to test different aspects of optical flow algorithms: (1) sequences with nonrigid motion where the ground-truth flow is determined by tracking hidden fluorescent texture, (2) realistic synthetic sequences, (3) high frame-rate video used to study interpolation error, and (4) modified stereo sequences of static scenes. In addition to the average angular error used by Barron et al., we compute the absolute flow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and in textureless regions. In October 2007, we published the performance of several well-known methods on a preliminary version of our data to establish the current state of the art. We also made the data freely available on the web at http://vision.middlebury.edu/flow/ . Subsequently a number of researchers have uploaded their results to our website and published papers using the data. A significant improvement in performance has already been achieved. In this paper we analyze the results obtained to date and draw a large number of conclusions from them.

References

[1]

Adiv, G. (1985). Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7 (4), 384-401.

Digital Library

[2]

Aggarwal, J., & Nandhakumar, N. (1988). On the computation of motion from sequences of images--a review. Proceedings of the IEEE, 76 (8), 917-935.

[3]

Anandan, P. (1989). A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2 (3), 283-310.

[4]

Anandan, P., & Weiss, R. (1985). Introducing smoothness constraint in a matching approach for the computation of displacement fields. In Proceedings of the DARPA image understanding workshop (pp. 186-196).

[5]

Baker, S., & Matthews, I. (2004). Lucas-Kanade 20 years on: a unifying framework. International Journal of Computer Vision, 46 (3), 221-255.

[6]

Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., & Szeliski, R. (2007). A database and evaluation methodology for optical flow. In Proceedings of the IEEE international conference on computer vision .

[7]

Barron, J., Fleet, D., & Beauchemin, S. (1994). Performance of optical flow techniques. International Journal of Computer Vision, 12 (1), 43-77.

Digital Library

[8]

Battiti, R., Amaldi, E., & Koch, C. (1991). Computing optical flow across multiple scales: an adaptive coarse-to-fine strategy. International Journal of Computer Vision, 6 (2), 133-145.

Digital Library

[9]

Beier, T., & Neely, S. (1992). Feature-based image metamorphosis. In Annual conference series: Vol. 26(2). ACM computer graphics, SIGGRAPH (pp. 35-42).

[10]

Bergen, J., Anandan, P., Hanna, K., & Hingorani, R. (1992). Hierarchical model-based motion estimation. In Proceedings of the European conference on computer vision (pp. 237-252).

[11]

Black, M., & Anandan, P. (1991). Robust dynamic motion estimation over time. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 296-302).

[12]

Black, M., & Anandan, P. (1996). The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding, 63 (1), 75-104.

Digital Library

[13]

Black, M., & Jepson, A. (1996). Estimating optical flow in segmented images using variable-order parametric models with local deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18 (10), 972-986.

Digital Library

[14]

Blake, A., & Zisserman, A. (1987). Visual reconstruction . Cambridge: MIT Press.

[15]

Bleyer, M., & Chambon, S. (2010). Does color really help in dense stereo matching? In Proceedings of the international symposium 3D data processing, visualization and transmission .

[16]

Bleyer, M., Rother, C., & Kohli, P. (2010). Surface stereo with soft segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition .

[17]

Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (11), 1222-1239.

Digital Library

[18]

Brox, T., Bregler, C., & Malik, J. (2009). Large displacement optical flow. In Proceedings of the IEEE conference on computer vision and pattern recognition .

[19]

Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004). High accuracy optical flow estimation based on a theory for warping. In Proceedings of the European Conference on Computer Vision (Vol. 4, pp. 25-36).

[20]

Bruhn, A., Weickert, J., & Schnörr, C. (2005). Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. International Journal of Computer Vision, 61 (3), 211-231.

[21]

Bruhn, A., Weickert, J., Kohlberger, T., & Schnörr, C. (2006). A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. International Journal of Computer Vision, 70 (3), 257-277.

Digital Library

[22]

Burt, P., Yen, C., & Xu, X. (1982). Local correlation measures for motion analysis: a comparative study. In Proceedings of the IEEE conference on pattern recognition and image processing (pp. 269-274).

[23]

Burt, P., Yen, C., & Xu, X. (1983). Multi-resolution flow-through motion analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 246-252).

[24]

Cassisa, C., Simoens, S., & Prinet, V. (2009). Two-frame optical flow formulation in an unwarped multiresolution scheme. In Proceedings of the Iberoamerican congress on pattern recognition (pp. 790-797).

[25]

Cooke, T. (2008). Two applications of graph-cuts to image processing. In Proceedings of digital image computing: techniques and applications (pp. 498-504).

[26]

DNA Research (2008). 3Delight rendering software. http://www.3delight.com/.

[27]

Enkelman, W. (1986). Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences. In Proceedings of the workshop on motion: representations and analysis (pp. 81-87).

[28]

Everingham, M., Van Gool, L., Williams, C., Winn, J., & Zisserman, A. (2009). The PASCAL visual object classes challenge 2009. http://www.pascal-network.org/challenges/VOC/voc2009/workshop/index.html

[29]

Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (4), 594-611.

Digital Library

[30]

Fleet, D., & Jepson, A. (1990). Computation of component image velocity from local phase information. International Journal of Computer Vision, 5 (1), 77-104.

Digital Library

[31]

Fuh, C., & Maragos, P. (1989). Region-based optical flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 130-135).

[32]

Georghiades, A., Belhumeur, P., & Kriegman, D. (2001). From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (6), 643-660.

Digital Library

[33]

Glazer, F., Reyonds, G., & Anandan, P. (1983). Scene matching by hierarchical correlation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 432-441).

[34]

Glocker, B., Paragios, N., Komodakis, N., Tziritas, G., & Navab, N. (2008). Optical flow estimation with uncertainties through dynamic MRFs. In Proceedings of the IEEE conference on computer vision and pattern recognition .

[35]

Golland, P., & Bruckstein, A. (1997). Motion from color. Computer Vision and Image Understanding, 68 (3), 346-362.

Digital Library

[36]

Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2008). Multi-PIE. In Proceedings of the international conference on automatic face and gesture recognition .

[37]

Hanna, K. (1991). Direct multi-resolution estimation of ego-motion and structure from motion. In Proceedings of the IEEE workshop on visual motion (pp. 156-162).

[38]

Haussecker, H., & Fleet, D. (2000). Computing optical flow with physical models of brightness variation. In Proceedings of the IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 760-767).

[39]

Herbst, E., Seitz, S., & Baker, S. (2009). Occlusion reasoning for temporal interpolation using optical flow . Technical report UW-CSE- 09-08-01, Department of Computer Science and Engineering University of Washington.

[40]

Horn, B. (1986). Robot vision . Cambridge: MIT Press.

[41]

Horn, B., & Schunck, B. (1981). Determining optical flow. Artificial Intelligence, 17 , 185-203.

Digital Library

[42]

Jepson, A., & Black, M. (1993). Mixture models for optical flow computation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 760-761).

[43]

Ju, S. (1998). Estimating image motion in layers: the skin and bones model . PhD thesis, Department of Computer Science, University of Toronto.

[44]

Ju, S., Black, M., & Jepson, A. (1996). Skin and bones: multi-layer, locally affine, optical flow and regularization of transparency. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 307-314).

[45]

Jung, H., Lee, K., & Lee, S. (2008). Toward global minimum through combined local minima. In Proceedings of the European conference on computer vision (Vol. 4, pp. 298-311).

[46]

Landis, H. (2002). Production-ready global illumination. In L. Gritz (Ed.), RenderMan in production: SIGGRAPH 2002 course 16 (pp. 87-100). New York: ACM.

[47]

Le Besnerais, G., & Champagnat, F. (2005). Dense optical flow by iterative local window registration. In Proceedings of the international conference on image processing (Vol. 1, pp. 137-140).

[48]

Lei, C., & Yang, Y. (2009). Optical flow estimation on coarse-to-fine region-trees using discrete optimization. In Proceedings of the IEEE international conference on computer vision .

[49]

Lempitsky, V., Roth, S., & Rother, C. (2008). Fusion flow: discrete-continuous optimization for optical flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition .

[50]

Levoy, M. (1988). Display of surfaces from volume data. IEEE Computer Graphics and Applications, 8 (3), 29-37.

Digital Library

[51]

Li, Y., & Huttenlocher, D. (2008). Learning for optical flow using stochastic optimization. In Proceedings of the European conference on computer vision (Vol. 2, pp. 373-391).

[52]

Liu, C., Freeman, W., Adelson, E., & Weiss, Y. (2008). Human-assisted motion annotation. In Proceedings of the IEEE conference on computer vision and pattern recognition .

[53]

Liu, C., Yuen, J., Torralba, A., Sivic, J., & Freeman, W. (2008). SIFT flow: dense correspondence across difference scenes. In Proceedings of the European conference on computer vision (Vol. 3, pp. 28-42).

[54]

Lucas, B., & Kanade, T. (1981). An iterative image registration technique with an application in stereo vision. In Proceedings of the international joint conference on artificial intelligence (pp. 674- 679).

[55]

Mahajan, D., Huang, F., Matusik, W., Ramamoorthi, R., & Belhumeur, P. (2009). Moving gradients: a path-based method for plausible image interpolation. In Annual conference series. ACM computer graphics, SIGGRAPH .

[56]

Markandey, V., & Flinchbaugh, B. (1990). Multispectral constraints for optical flow computation. In Proceedings of the IEEE international conference on computer vision (pp. 38-41).

[57]

McCane, B., Novins, K., Crannitch, D., & Galvin, B. (2001). On benchmarking optical flow. Computer Vision and Image Understanding, 84 (1), 126-143.

Digital Library

[58]

Mitiche, A., & Bouthemy, P. (1996). Computation and analysis of image motion: a synopsis of current problems and methods. International Journal of Computer Vision, 19 (1), 29-55.

Digital Library

[59]

Mova LLC (2004). Contour reality capture. http://www.mova.com/.

[60]

Murray, D., & Buxton, B. (1987). Scene segmentation from visual motion using global optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9 (2), 220-228.

Digital Library

[61]

Nagel, H.-H., & Enkelmann, W. (1986). An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (5), 565-593.

Digital Library

[62]

Negahdaripour, S. (1998). Revised definition of optical flow: integration of radiometric and geometric cues for dynamic scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (9), 961-979.

Digital Library

[63]

Nir, T., Bruckstein, A., & Kimmel, R. (2008). Over-parameterized variational optical flow. International Journal of Computer Vision, 76 (2), 205-216.

Digital Library

[64]

Ohta, N. (1989). Optical flow detection by color images. In International conference on image processing (pp. 801-805).

[65]

Otte, M., & Nagel, H.-H. (1994). Optical flow estimation: advances and comparisons. In Proceedings of the European conference on computer vision (pp. 51-60).

[66]

Philips, P., Scruggs, W., O'Toole, A., Flynn, P., Bowyer, K., Schott, C., & Sharpe, M. (2005). Overview of the face recognition grand challenge. In Proceedings of the IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 947-954).

[67]

Pock, T., Pock, M., & Bischof, H. (2007). Algorithmic differentiation: application to variational problems in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (7), 1180-1193.

Digital Library

[68]

Pratt, W. (1974). Correlation techniques of image registration. IEEE Transactions on Aerospace and Electronic Systems, AES-10 , 353- 358.

[69]

Ramnath, K., Baker, S., Matthews, I., & Ramanan, D. (2008). Increasing the density of active appearance models. In Proceedings of the IEEE conference on computer vision and pattern recognition .

[70]

Rannacher, J. (2009). Realtime 3D motion estimation on graphics hardware . Undergraduate thesis, Heidelberg University.

[71]

Ren, X. (2008). Local grouping for optical flow. In Proceedings of the IEEE conference on computer vision and pattern recognition .

[72]

Roth, S., & Black, M. (2007). On the spatial statistics of optical flow. International Journal of Computer Vision, 74 (1), 33-50.

Digital Library

[73]

Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47 (13), 7-42.

[74]

Scharstein, D., & Szeliski, R. (2003). High-accuracy stereo depth maps using structured light. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 195-202).

[75]

Seitz, S., & Baker, S. (2009). Filter flow. In Proceedings of the IEEE international conference on computer vision .

[76]

Seitz, S., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. In Proceedings of the IEEE conference on computer vision and pattern recognition (Vol. 1, pp. 519-526).

[77]

Shade, J., Gortler, S., He, L.-W., & Szeliski, R. (1998). Layered depth images. In Annual conference series. ACM computer graphics, SIGGRAPH (pp. 231-242).

[78]

Shizawa, M., & Mase, K. (1991). A unified computational theory for motion transparency and motion boundaries based on eigenenergy analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 289-295).

[79]

Sim, T., Baker, S., & Bsat, M. (2003). The CMU pose, illumination, and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (12), 1615-1618.

Digital Library

[80]

Stiller, C., & Konrad, J. (1999). Estimating motion in image sequences: a tutorial on modeling and computation of 2D motion. IEEE Signal Processing Magazine, 16 (4), 70-91.

[81]

Sun, C. (1999). Fast optical flow using cross correlation and shortest-path techniques. In Proceedings of digital image computing: techniques and applications (pp. 143-148).

[82]

Sun, J., Shum, H.-Y., & Zheng, N. (2003). Stereo matching using belief propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (7), 787-800.

Digital Library

[83]

Sun, D., Roth, S., Lewis, J., & Black, M. (2008). Learning optical flow. In Proceedings of the European conference on computer vision (Vol. 3, pp. 83-97).

[84]

Sun, D., Roth, S., & Black, M. (2010). Secrets of optical flow estimation and their principles. In Proceedings of the IEEE conference on computer vision and pattern recognition .

[85]

Szeliski, R. (1999). Prediction error as a quality metric for motion and stereo. In Proceedings of the IEEE international conference on computer vision (pp. 781-788).

[86]

Tappen, M., Adelson, E., & Freeman, W. (2006). Estimating intrinsic component images using non-linear regression. In Proceedings of the IEEE conference on computer vision and pattern recognition (Vol. 2, pp. 1992-1999).

[87]

Trobin, W., Pock, T., Cremers, D., & Bischof, H. (2008). Continuous energy minimization via repeated binary fusion. In Proceedings of the European conference on computer vision (Vol. 4, pp. 677- 690).

[88]

Trobin, W., Pock, T., Cremers, D., & Bischof, H. (2008). An unbiased second-order prior for high-accuracy motion estimation. In Proceedings of pattern recognition, DAGM (pp. 396-405).

[89]

Valgaerts, L., Bruhn, A., & Weickert, J. (2008). A variational model for the joint recovery of the fundamental matrix and the optical flow. In Proceedings of pattern recognition, DAGM (pp. 314-324).

[90]

Vedula, S., Baker, S., Rander, P., Collins, R., & Kanade, T. (2005). Three-dimensional scene flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (3), 475-480.

Digital Library

[91]

Wang, J., & Adelson, E. (1993). Layered representation for motion analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 361-366).

[92]

Wedel, A., Pock, T., Braun, J., Franke, U., & Cremers, D. (2008). Duality TV-L1 flow with fundamental matrix prior. In Proceedings of image and vision computing , New Zealand.

[93]

Wedel, A., Pock, T., Zach, C., Cremers, D., & Bischof, H. (2008). An improved algorithm for TV-L1 optical flow. In Proceedings of the Dagstuhl motion workshop .

[94]

Wedel, A., Cremers, D., Pock, T., & Bischof, H. (2009). Structure-and motion-adaptive regularization for high accuracy optic flow. In Proceedings of the IEEE international conference on computer vision .

[95]

Weiss, Y. (1997). Smoothness in layers: motion segmentation using nonparametric mixture estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 520- 526).

[96]

Werlberger, M., Trobin, W., Pock, T., Bischof, H., Wedel, A., & Cremers, D. (2009). Anisotropic Huber-L1 optical flow. In Proceedings of the British machine vision conference .

[97]

Xu, L., Chen, J., & Jia, J. (2008). A segmentation based variational model for accurate optical flow estimation. In Proceedings of the European conference on computer vision (Vol. 1, pp. 671-684).

[98]

Zimmer, H., Bruhn, A., Weickert, J., Valgaerts, L., Salgado, A., Rosenhahn, B., & Seidel, H.-P. (2009). Complementary optic flow. In Proceedings of seventh international workshop on energy minimization methods in computer vision and pattern recognition .

[99]

Zitnick, C., Kang, S., Uyttendaele, M., Winder, S., & Szeliski, R. (2004). High-quality video view interpolation using a layered representation. In Annual conference series: Vol. 23(2). ACM computer graphics, SIGGRAPH (pp. 600-608).

Cited By

Pagnanelli GZinelli LLepora NCatalano MBicchi ABianchi M(2025)Integrating Human-Like Impedance Regulation and Model-Based Approaches for Compliance Discrimination via Biomimetic Optical Tactile SensorsIEEE Transactions on Robotics10.1109/TRO.2024.352214941(857-870)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TRO.2024.3522149
Zhang BSuo JDai Q(2025)Event-Enhanced Snapshot Compressive Videography at 10K FPSIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.349678847:2(1266-1278)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TPAMI.2024.3496788
Kowal MSiam MIslam MBruce NWildes RDerpanis K(2025)Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.346229147:1(190-205)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TPAMI.2024.3462291
Show More Cited By

A Database and Evaluation Methodology for Optical Flow
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Computer graphics
    1. Animation

Recommendations

Motion Field and Optical Flow: Qualitative Properties

It is shown that the motion field the 2-D vector field which is the perspective projection on the image plane of the 3-D velocity field of a moving scene, and the optical flow, defined as the estimate of the motion field which can be derived from the ...
Optical flow estimation for motion-compensated compression

The computation of optical flow within an image sequence is one of the most widely used techniques in computer vision. In this paper, we present a new approach to estimate the velocity field for motion-compensated compression. It is derived by a ...
Automatic generation of dense non-rigid optical flow
Abstract
There hardly exists any large-scale datasets with dense optical flow of non-rigid motion from real-world imagery as of today. The reason lies mainly in the required setup to derive ground truth optical flows: a series of images with ...
Graphical abstract

Display Omitted
Highlights
- First method to automatically generate dense non-rigid optical flow data for training.

Comments

Information & Contributors

Information

Published In

cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 92, Issue 1

March 2011

129 pages

ISSN:0920-5691

Issue’s Table of Contents

Copyright © Copyright © 2011 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2011

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

363
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Pagnanelli GZinelli LLepora NCatalano MBicchi ABianchi M(2025)Integrating Human-Like Impedance Regulation and Model-Based Approaches for Compliance Discrimination via Biomimetic Optical Tactile SensorsIEEE Transactions on Robotics10.1109/TRO.2024.352214941(857-870)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TRO.2024.3522149
Zhang BSuo JDai Q(2025)Event-Enhanced Snapshot Compressive Videography at 10K FPSIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.349678847:2(1266-1278)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TPAMI.2024.3496788
Kowal MSiam MIslam MBruce NWildes RDerpanis K(2025)Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.346229147:1(190-205)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TPAMI.2024.3462291
Kim YKwon SKang DLee HPaik J(2025)Enhancing video frame interpolation with region of motion loss and self-attention mechanismsNeurocomputing10.1016/j.neucom.2024.128728614:COnline publication date: 21-Jan-2025
https://dl.acm.org/doi/10.1016/j.neucom.2024.128728
Zhao RXiong RZhang JZhang XYu ZHuang TWooldridge MDy JNatarajan S(2024)Optical flow for spike camera with hierarchical spatial-temporal spike fusionProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i7.28581(7496-7504)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i7.28581
Tang CSheng XLi ZZhang HLi LLiu DWooldridge MDy JNatarajan S(2024)Offline and online optical flow enhancement for deep video compressionProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i6.28317(5118-5126)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i6.28317
Danier DZhang FBull DWooldridge MDy JNatarajan S(2024)LDMVFIProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i2.27912(1472-1480)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i2.27912
Luo YYang MTang JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Dual-view Pyramid Network for Video Frame InterpolationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681555(6364-6373)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681555
Shen JHu KBao WChen CWang ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Bridging the Gap: Sketch-Aware Interpolation Network for High-Quality Animation Sketch InbetweeningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681146(10287-10295)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681146
Lyu ZLi MJiao JChen CCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Frame Interpolation with Consecutive Brownian Bridge DiffusionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680961(3449-3458)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680961
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents