Efektivitas Pengembangan Quick Rigging Tools dalam Pembuatan Film Animasi 3D

Herin Dwibima Aprianto, Ahmad Rifa'i, Anip Moniva

Abstract


Proses rigging merupakan tahap penting dalam produksi film animasi 3D karena menentukan kesiapan karakter untuk dianimasikan, kualitas deformasi mesh, serta efisiensi kerja animator. Pada pipeline manual, pembuatan skeleton, kontrol IK/FK, penamaan joint, mirroring, dan inisialisasi skin weight sering memerlukan waktu panjang serta berpotensi menimbulkan inkonsistensi teknis. Penelitian ini bertujuan untuk mengevaluasi Quick Rigging Tools sebagai pendekatan semiotomatis untuk mendukung pembuatan rig karakter 3D pada Autodesk Maya Rigging Builder. Metode penelitian menggunakan pendekatan kuantitatif komparatif berbasis pengujian gerak. Data sudut gerak diperoleh dari satu skenario animasi tendangan dengan empat bagian tubuh, yaitu tangan kanan, tangan kiri, kaki kanan, dan kaki kiri, masing-masing 69 frame. Sudut hasil rigging QR Maya dan rig Studio dibandingkan dengan video referensi menggunakan circular angular error. Analisis statistik mencakup rata-rata ± standar deviasi, RMSE, uji normalitas Shapiro-Wilk, serta uji Wilcoxon signed-rank karena selisih error tidak berdistribusi normal. Hasil menunjukkan error gabungan QR Maya sebesar 10,718 ± 9,380 derajat, sedangkan studio sebesar 11,236 ± 9,715 derajat. Perbedaan kualitas gerak tidak signifikan secara statistik (Wilcoxon, p = 0,787), sehingga Quick Rigging Tools belum dapat dinyatakan lebih unggul secara statistik dibandingkan dengan metode pembanding pada aspek kualitas gerak animasi. Meskipun demikian, temuan deskriptif menunjukkan bahwa Quick Rigging Tools memiliki potensi untuk meningkatkan efisiensi workflow, konsistensi rig, dan kesiapan pipeline melalui standardisasi skeleton, controller, penamaan, dan validasi rig.


Keywords


(Animasi 3D; Film Animasi; Otomasi Produksi; Quick Rigging Tools; Rigging Karakter; Skinning).

References


Z. Xu, Y. Zhou, E. Kalogerakis, C. Landreth, and K. Singh, 2020, "RigNet: Neural Rigging for Articulated Characters," ACM Transactions on Graphics. DOI: https://doi.org/10.1145/3386569.3392379

J. Ma and D. Zhang, 2023, "TARig: Adaptive Template-Aware Neural Rigging for Humanoid Characters," Computers & Graphics. DOI: https://doi.org/10.1016/j.cag.2023.05.018

Z. Chu, F. Xiong, M. Liu, J. Zhang, M. Shao, Z. Sun, D. Wang, and M. Xu, 2025, "HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. DOI: https://doi.org/10.1109/CVPR52734.2025.00037

Z. Guo, J. Xiang, K. Ma, W. Zhou, H. Li, and R. Zhang, 2024, "Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters," arXiv. DOI: https://doi.org/10.48550/arXiv.2411.18197

J. Kim, H. Son, J. Bae, and Y. M. Kim, 2021, "Auto-rigging 3D Bipedal Characters in Arbitrary Poses," Eurographics 2021 Short Papers. DOI: https://doi.org/10.2312/egs.20211023

I. Liu, Z. Xu, W. Yifan, H. Tan, Z. Xu, X. Wang, H. Su, and Z. Shi, 2025, "RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets," arXiv. DOI: https://doi.org/10.48550/arXiv.2502.09615

M. Sun, S. Mao, K. Chen, Y. Chen, S. Lu, J. Wang, J. Dong, and R. Huang, 2025, "ARMO: Autoregressive Rigging for Multi-Category Objects," Proceedings of the IEEE/CVF International Conference on Computer Vision. Link: https://openaccess.thecvf.com/content/ICCV2025/html/Sun_ARMO_Autoregressive_Rigging_for_Multi-Category_Objects_ICCV_2025_paper.html

P. Li, K. Aberman, R. Hanocka, L. Liu, O. Sorkine-Hornung, and B. Chen, 2021, "Learning Skeletal Articulations with Neural Blend Shapes," ACM Transactions on Graphics. DOI: https://doi.org/10.1145/3450626.3459852

X. Chen, Y. Zheng, M. J. Black, O. Hilliges, and A. Geiger, 2021, "SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes," Proceedings of the IEEE/CVF International Conference on Computer Vision. DOI: https://doi.org/10.1109/ICCV48922.2021.01139

Y. Kant, A. Siarohin, R. A. Guler, M. Chai, J. Ren, S. Tulyakov, and I. Gilitschenski, 2023, "Invertible Neural Skinning," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. DOI: https://doi.org/10.1109/CVPR52729.2023.00842

C. Song, X. Li, F. Yang, Z. Xu, J. Wei, F. Liu, J. Feng, G. Lin, and J. Zhang, 2025, "Puppeteer: Rig and Animate Your 3D Models," arXiv. DOI: https://doi.org/10.48550/arXiv.2508.10898

Z. Liao, V. Golyanik, M. Habermann, and C. Theobalt, 2024, "VINECS: Video-based Neural Character Skinning," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Link: https://openaccess.thecvf.com/content/CVPR2024/papers/Liao_VINECS_Video-based_Neural_Character_Skinning_CVPR_2024_paper.pdf

Z. Xu, Y. Zhou, L. Yi, and E. Kalogerakis, 2022, "MoRig: Motion-Aware Rigging of Character Meshes from Point Clouds," SIGGRAPH Asia. Link: https://arxiv.org/abs/2210.09463

S. Nuvoli, N. Pietroni, P. Cignoni, R. Scateni, and M. Tarini, 2022, "SkinMixer: Blending 3D Animated Models," ACM Transactions on Graphics. DOI: https://doi.org/10.1145/3550454.3555503

S. Lee and C. K. Liu, 2024, "Creating a 3D Mesh in A-pose from a Single Image for Character Rigging," Computer Graphics Forum. DOI: https://doi.org/10.1111/cgf.15177

Z. Yang, S. Wang, S. Manivasagam, Z. Huang, W.-C. Ma, X. Yan, E. Yumer, and R. Urtasun, 2021, "S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. DOI: https://doi.org/10.1109/CVPR46437.2021.01308

S. Saito, J. Yang, Q. Ma, and M. J. Black, 2021, "SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. DOI: https://doi.org/10.1109/CVPR46437.2021.00291

O. Benchekroun, J. E. Zhang, S. Chaudhuri, E. Grinspun, Y. Zhou, and A. Jacobson, 2023, "Fast Complementary Dynamics via Skinning Eigenmodes," ACM Transactions on Graphics. DOI: https://doi.org/10.1145/3592404

Y. Wu and N. Umetani, 2023, "Two-Way Coupling of Skinning Transformations and Position Based Dynamics," Proceedings of the ACM on Computer Graphics and Interactive Techniques. DOI: https://doi.org/10.1145/3606930

M. Zheng, Y. Zhou, D. Ceylan, and J. Barbič, 2021, "A Deep Emulator for Secondary Motion of 3D Characters," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Link: https://arxiv.org/abs/2103.01261

J. E. Zhang, S. Bang, D. I. W. Levin, and A. Jacobson, 2020, "Complementary Dynamics," ACM Transactions on Graphics. Link: https://arxiv.org/abs/2009.02462

M. Fabian, P. Chalmovianský, and M. Bátorová, 2024, "Homotopy Based Skinning of Spheres," Computer-Aided Design. DOI: https://doi.org/10.1016/j.cad.2024.103686

C. Lin, C. Li, Y. Liu, N. Chen, Y.-K. Choi, and W. Wang, 2021, "Point2Skeleton: Learning Skeletal Representations from Point Clouds," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Link: https://arxiv.org/abs/2012.00230

F. Kokkinos and I. Kokkinos, 2021, "Learning Monocular 3D Reconstruction of Articulated Categories from Motion," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. DOI: https://doi.org/10.1109/CVPR46437.2021.00178

L. Liu, M. Habermann, V. Rudnev, K. Sarkar, J. Gu, and C. Theobalt, 2021, "Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control," ACM Transactions on Graphics. Link: https://arxiv.org/abs/2106.02019




DOI: https://doi.org/10.55340/jiu.v15i1.2701

Refbacks

  • There are currently no refbacks.


__________________________________________________________________________________________________________________________________________________

SK Accreditation No. 286/DST/C3/HM.01.00/2026 Tanggal 7 April 2026

Editorial Address :

Program Studi Teknik Informatika, Fakultas Teknik, Universitas Dayanu Ikhsanuddin Jl. Dayanu Ikhsanuddin no.124 Baubau, Sulawesi Tenggara 

Jurnal Informatika by Program Studi Teknik Informatika, Fakultas Teknik, Universitas Dayanu Ikhsanuddin Baubau, Indonesia is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Based on work at https://ejournal.unidayan.ac.id/index.php/JIU