A comparative analysis of transitions generated using the Unity game development platform
Abstract
This paper conducts a comparative analysis of transitions generated using the Unity engine. It selects fifteen animations featuring a humanoid character, introduces breaks in marker trajectories, and fills them with transitions generated by the game engine's animator. These transitions are then compared with the unmodified original character animation. The study compares animations by calculating the average deviation in bone rotation and position between the original and generated motion throughout the animation. The results show that the Unity engine excels in generating transitions for slow animations involving the lower body limbs, with the largest errors occurring in the bones at the extremities of the limbs.
Keywords:
Unity, animation, character motion, animation qualityReferences
M. Masuch, N. Röber, Game graphics beyond realism: Then, now and tomorrow, Level UP: digital games research conference (DIGRA), Faculty of Arts, University of Utrecht, 2004, http://www.digra.org/wp-content/uploads/digital-library/05150.48223.pdf.
Google Scholar
J.K. Hodgins, J.F. O'Brien, J. Tumblin, Perception of human motion with different geometric models, IEEE Transactions on Visualization and Computer Graphics 4 (4) (1998) 307-316, https://doi.org/10.1109/2945.765325.
Google Scholar
M. Oesker, H. Hecht, B. Jung, Psychological Evidence for Unconscious Processing of Detail in Real‐time Animation of Multiple Characters, The Journal of Visualization and Computer Animation 11 (2) (2000) 105-112, https://doi.org/10.1002/1099-1778(200005)11:2<105::AID-VIS222>3.0.CO;2-Q.
Google Scholar
J. Lee, J. Chai, P.S. Reitsma, J.K. Hodgins, N.S. Pollard, Interactive control of avatars animated with human motion data, ACM Transactions on Graphics 21 (3) (2002) 491-500, https://doi.org/10.1145/566654.566607.
Google Scholar
C. Rose, B. Guenter, B. Bodenheimer, M.F. Cohen, Efficient generation of motion transitions using spacetime constraints, In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques (1996) 147-154, https://doi.org/10.1145/237170.237229.
Google Scholar
T. Polichroniadis, N. Dodgson, Motion blending using a classifier system, In Proceedings of the 7th International Conference in Central Europe on Computer Graphics 1 (1999) 225-232, http://www.neildodgson.com/pubs/WSCG99.pdf.
Google Scholar
G. Ashraf, K.C. Wong, Generating consistent motion transition via decoupled framespace interpolation, Blackwell Publishers Ltd. Oxford, UK and Boston, USA, Computer Graphics Forum 19 (3) (2000) 447-456, https://doi.org/10.1111/1467-8659.00437.
Google Scholar
L. Kovar, M. Gleicher, Flexible automatic motion blending with registration curves, In Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation (SCA '03), Eurographics Association, Goslar, DEU, (2003) 214–224, http://dx.doi.org/10.2312/SCA03/214-224.
Google Scholar
M. Gleicher, H.J. Shin, L. Kovar, A. Jepsen, Snap-together motion: assembling run-time animations, ACM SIGGRAPH (2008) 1-9, https://doi.org/10.1145/641480.641515.
Google Scholar
V.B. Zordan, A. Majkowska, B. Chiu, M. Fast, Dynamic response for motion capture animation, ACM Transactions on Graphics 24 (3) (2005) 697-701, https://doi.org/10.1145/1073204.1073249.
Google Scholar
H.J. Shin, H.S. Oh, Fat graphs: constructing an interactive character with continuous controls, In Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation (2006) 291-298, http://dx.doi.org/10.2312/SCA/SCA06/291-298.
Google Scholar
D. Holden, T. Komura, J. Saito, Phase-functioned neural networks for character control, ACM Transactions on Graphics (TOG), 36 (4) (2017) 1-13, https://doi.org/10.1145/3072959.3073663.
Google Scholar
F. Gaisbauer, P. Fröhlich, J. Lehwald, P. Agethen, E. Rukzio, Presenting a Deep Motion Blending Approach for Simulating Natural Reach Motions, Eurographics (Posters) (2018) 5-6, http://dx.doi.org/10.2312/egp.20181010.
Google Scholar
F. Gaisbauer, J. Lehwald, J. Sprenger, E. Rukzio, Natural posture blending using deep neural networks, In Proceedings of the 12th ACM SIGGRAPH Conference on Motion, Interaction and Games (2019) 1-6, https://doi.org/10.1145/3359566.3360052.
Google Scholar
Dokumentacja Unity dotycząca generowania przejść, https://docs.unity3d.com/Manual/class-Transition.html, [01.10.2023].
Google Scholar
Baza danych animacji używanych w badaniu, http://mocap.cs.cmu.edu/, [01.10.2023].
Google Scholar
Informacje dotyczące przekonwertowania jednostek miary w animacjach na centymetry, http://mocap.cs.cmu.edu/faqs.php, [01.10.2023].
Google Scholar
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