MSVC - Assignment

Motion Synthesis for Virtual Characters

Seminar an der Universität des Saarlandes, Fachrichtung Informatik, LSF 116960

Please note: The dates are not finalized, yet, and are therefore not confirmed.

Responsible superwisers are given in brackets.

Contact Information:
Erik Herrmann: erik.herrmann@dfki.de
Han Du: han_h.du@dfki.de
Janis Sprenger: janis.sprenger@dfki.de
Noshaba Cheema: noshaba.cheema@dfki.de
Somayeh Hosseini: Seyedeh_Somayeh.Hosseini@dfki.de

The current assignment of topics and dates to pre-registered participants for presentations and their opponents is as follows:

Topic#

Topic

Date

Opponent

0

Seminar Kickoff Session

Presentation and discussion of the final seminar schedule.
Final explanations regarding presentations and regulations.

K. Fischer

10.4.2019

n/a

n/a

1

Data-driven synthesis based on Motion Graphs

Kovar, Lucas, Michael Gleicher, and Frédéric Pighin. "Motion graphs." ACM SIGGRAPH 2008 classes. ACM, 2008.

V. Muthukumar (S. Hosseini)

17.4.2019

M. Baig

D. David

2

Data-driven synthesis based on Statistical Motion Graphs

Lee, Jehee, et al. "Interactive control of avatars animated with human motion data." ACM Transactions on Graphics (ToG). Vol. 21. No. 3. ACM, 2002.

M. H. Jamil ( H. Du )

17.4.2019

Y. Kasture

M. Baig

3

Data-driven synthesis based on Motion Fields

Lee, Yongjoon, et al. "Motion fields for interactive character locomotion." ACM Transactions on Graphics (TOG). Vol. 29. No. 6. ACM, 2010.

D. David (E. Herrmann)

24.4.2019

H. Liang

V. Muthukumar

5

Interactive Data-driven synthesis based on Fully Connected Neural Networks

Lee, Kyungho, Seyoung Lee, and Jehee Lee. "Interactive character animation by learning multi-objective control." SIGGRAPH Asia 2018 Technical Papers. ACM, 2018.

M. Baig (J. Sprenger)

2.5.2019

S. Mehboob

M. M. Sakha

6

Motion synthesis based on generative Neural Networks

Barsoum E, Kender J, Liu Z. HP-GAN: Probabilistic 3D human motion prediction via GAN. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 2018 (pp. 1418-1427).

H. Liang (H. Du)

2.5.2019

W. Liu

G. Tiwari

7

Training locomotion controllers using reinforcement learning

Xue Bin Peng, Glen Berseth, Michiel van de Panne (2016): "Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning". ACM Transactions on Graphics (TOG) [Proc. SIGGRAPH 2016].

C. Bhuvaneshwara (N. Cheema)

15.5.2019

D. David

T. Yenamandra

8

Training physical model controllers using sampling based optimization

Liu, Libin, Michiel Van De Panne, and KangKang Yin. "Guided learning of control graphs for physics-based characters." *ACM Transactions on Graphics (TOG)* 35.3 (2016): 29.

T. Yenamandra (E. Herrmann)

15.5.2019

H. Liang

M. Sakha

9

Motion Capturing

von Marcard, Timo, et al. "Recovering accurate 3d human pose in the wild using imus and a moving camera." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

K. Chowdhury (J. Sprenger)

22.5.2019

S. Mehboob

W. Liu

4

Data-driven approaches based on Convolutional Neural Networks

Holden, Daniel, Jun Saito, and Taku Komura. "A deep learning framework for character motion synthesis and editing." ACM Transactions on Graphics (TOG) 35.4 (2016): 138.

S. Mehboob (N. Cheema)

22.5.2019

C. Bhuvaneshwara

T. Yenamandra

10

Imitating motion capture data with a physical model using reinforcement learning

Peng, Xue Bin, et al. "DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills." arXiv preprint arXiv:1804.02717 (2018).

G. Tiwari (N. Cheema)

29.5.2019

K. Chowdhury

V. Singh Sidhu

11

Motion editing using space-time constraints

Gleicher, Michael. "Retargeting motion to new characters." Proceedings of the 25th annual conference on Computer graphics and interactive techniques. ACM, 1998.

P. Uttarwar (E. Herrmann)

29.5.2019

Y. Kasture

M. H. Jami

12

Motion recognition and segmentation using neural networks

Andreas Aristidou, Daniel Cohen-Or, Jessica K. Hodgins, Jessica K. Hodgins and Jessica K. Hodgins. "Deep motifs and motion signatures". ACM Transactions on Graphics (TOG). Vol. 37 Issue 6, November 2018 Article No. 187.

Y. Kasture (S. Hosseini)

5.6.2019

P. Uttarwar

M. H. Jami

13

Unsupervised motion capture segmentation

Anna Vögele, Björn Krüger and Reinhard Klein. "Efficient Unsupervised Temporal Segmentation of Human Motion". In Proceedings of ACM Symposium of Computer Animation 2014.

V. Singh Sidhu (S. Hosseini)

5.6.2019

P. Uttarwar

K. Chowdhury

14

Motion Style Transfer

Daniel Holden, Ikhsanul Habibie, Ikuo Kusajima, and Taku Komura.2017. Fast Neural Style Transfer for Motion Data.IEEE computergraphics and applications37, 4 (2017), 42-49.

W. Liu (H. Du)

26.6.2019

C. Bhuvaneshwara

V. Singh Sidhu

15

Motion Editing using Neural Networks

Villegas, Ruben, et al. "Neural kinematic networks for unsupervised motion retargetting." IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vol. 3. 2018.

M. M. Sakha (J. Sprenger)

26.6.2019

V. Muthukumar

G. Tiwari

Impressum

Topic#	Topic	Date	Opponent
0	Seminar Kickoff Session Presentation and discussion of the final seminar schedule. Final explanations regarding presentations and regulations. *K. Fischer*	10.4.2019	n/a n/a
1	Data-driven synthesis based on Motion Graphs Kovar, Lucas, Michael Gleicher, and Frédéric Pighin. "Motion graphs." ACM SIGGRAPH 2008 classes. ACM, 2008. *V. Muthukumar (S. Hosseini)*	17.4.2019	M. Baig D. David
2	Data-driven synthesis based on Statistical Motion Graphs Lee, Jehee, et al. "Interactive control of avatars animated with human motion data." ACM Transactions on Graphics (ToG). Vol. 21. No. 3. ACM, 2002. *M. H. Jamil ( H. Du )*	17.4.2019	Y. Kasture M. Baig
3	Data-driven synthesis based on Motion Fields Lee, Yongjoon, et al. "Motion fields for interactive character locomotion." ACM Transactions on Graphics (TOG). Vol. 29. No. 6. ACM, 2010. *D. David (E. Herrmann)*	24.4.2019	H. Liang V. Muthukumar
5	Interactive Data-driven synthesis based on Fully Connected Neural Networks Lee, Kyungho, Seyoung Lee, and Jehee Lee. "Interactive character animation by learning multi-objective control." SIGGRAPH Asia 2018 Technical Papers. ACM, 2018. *M. Baig (J. Sprenger)*	2.5.2019	S. Mehboob M. M. Sakha
6	Motion synthesis based on generative Neural Networks Barsoum E, Kender J, Liu Z. HP-GAN: Probabilistic 3D human motion prediction via GAN. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 2018 (pp. 1418-1427). *H. Liang (H. Du)*	2.5.2019	W. Liu G. Tiwari
7	Training locomotion controllers using reinforcement learning Xue Bin Peng, Glen Berseth, Michiel van de Panne (2016): "Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning". ACM Transactions on Graphics (TOG) [Proc. SIGGRAPH 2016]. *C. Bhuvaneshwara (N. Cheema)*	15.5.2019	D. David T. Yenamandra
8	Training physical model controllers using sampling based optimization Liu, Libin, Michiel Van De Panne, and KangKang Yin. "Guided learning of control graphs for physics-based characters." ACM Transactions on Graphics (TOG) 35.3 (2016): 29. *T. Yenamandra (E. Herrmann)*	15.5.2019	H. Liang M. Sakha
9	Motion Capturing von Marcard, Timo, et al. "Recovering accurate 3d human pose in the wild using imus and a moving camera." Proceedings of the European Conference on Computer Vision (ECCV). 2018. *K. Chowdhury (J. Sprenger)*	22.5.2019	S. Mehboob W. Liu
4	Data-driven approaches based on Convolutional Neural Networks Holden, Daniel, Jun Saito, and Taku Komura. "A deep learning framework for character motion synthesis and editing." ACM Transactions on Graphics (TOG) 35.4 (2016): 138. *S. Mehboob (N. Cheema)*	22.5.2019	C. Bhuvaneshwara T. Yenamandra
10	Imitating motion capture data with a physical model using reinforcement learning Peng, Xue Bin, et al. "DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills." arXiv preprint arXiv:1804.02717 (2018). *G. Tiwari (N. Cheema)*	29.5.2019	K. Chowdhury V. Singh Sidhu
11	Motion editing using space-time constraints Gleicher, Michael. "Retargeting motion to new characters." Proceedings of the 25th annual conference on Computer graphics and interactive techniques. ACM, 1998. *P. Uttarwar (E. Herrmann)*	29.5.2019	Y. Kasture M. H. Jami
12	Motion recognition and segmentation using neural networks Andreas Aristidou, Daniel Cohen-Or, Jessica K. Hodgins, Jessica K. Hodgins and Jessica K. Hodgins. "Deep motifs and motion signatures". ACM Transactions on Graphics (TOG). Vol. 37 Issue 6, November 2018 Article No. 187. *Y. Kasture (S. Hosseini)*	5.6.2019	P. Uttarwar M. H. Jami
13	Unsupervised motion capture segmentation Anna Vögele, Björn Krüger and Reinhard Klein. "Efficient Unsupervised Temporal Segmentation of Human Motion". In Proceedings of ACM Symposium of Computer Animation 2014. *V. Singh Sidhu (S. Hosseini)*	5.6.2019	P. Uttarwar K. Chowdhury
14	Motion Style Transfer Daniel Holden, Ikhsanul Habibie, Ikuo Kusajima, and Taku Komura.2017. Fast Neural Style Transfer for Motion Data.IEEE computergraphics and applications37, 4 (2017), 42-49. *W. Liu (H. Du)*	26.6.2019	C. Bhuvaneshwara V. Singh Sidhu
15	Motion Editing using Neural Networks Villegas, Ruben, et al. "Neural kinematic networks for unsupervised motion retargetting." IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vol. 3. 2018. *M. M. Sakha (J. Sprenger)*	26.6.2019	V. Muthukumar G. Tiwari