## References

Agisoft Photoscan 1.0.4 (2014). http://www.agisoft.ru/products/photoscan

Agudo, A., Agapito, L., Calvo, B., & Montiel, J. M. (2014). Good vibrations: A modal analysis approach for sequential non-rigid structure from motion. In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pp. 1558–1565

Agudo, A., Moreno-Noguer, F., Calvo, B., & Montiel, J. M. M. (2016). Sequential non-rigid structure from motion using physical priors. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 38(5), 979–994.

Bartoli, A., Gérard, Y., Chadebecq, F., Collins, T., & Pizarro, D. (2015). Shape-from-template. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 37(10), 2099–2118.

Bartoli, A., Perriollat, M., & Chambon, S. (2010). Generalized thin-plate spline warps. *International Journal of Computer Vision*, 88(1), 85–110.

Bay, H., Tuytelaars, T., Van Gool, L. (2006). Surf: Speeded up robust features. In *European conference on computer vision*, pp. 404–417. Springer.

Blender (1994). Online Community: Blender - a 3D modelling and rendering package. Blender Foundation, Blender Institute, Amsterdam. http://www.blender.org

Bookstein, F. L. (1989). Principal warps: Thin-plate splines and the decomposition of deformations. *IEEE Transactions on Pattern Analysis & Machine Intelligence*, 6, 567–585.

Brunet, F., Bartoli, A., & Hartley, R. I. (2014). Monocular template-based 3D surface reconstruction: Convex inextensible and non-convex isometric methods. *Computer Vision and Image Understanding*, 125, 138–154.

Brunet, F., Bartoli, A., Navab, N., Malgouyres, R. et al. (2009). NURBS warps. In *British machine vision conference*.

Casillas-Perez, D., Pizarro, D., Fuentes-Jimenez, D., Mazo, M., & Bartoli, A. (2018). Equiareal shape-from-template. *Journal of Mathematical Imaging and Vision*, https://doi.org/10.1007/s10851-018-0862-5.

Chhatkuli, A., Pizarro, D., Bartoli, A., & Collins, T. (2017). A stable analytical framework for isometric shape-from-template by surface integration. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 39(5), 833–850.

Do Carmo, M. P. (2016). *Differential geometry of curves and surfaces: Revised and updated second edition*. Courier Dover Publications.

Dubitzky, W., Granzow, M., & Berrar, D. P. (2007). *Fundamentals of data mining in genomics and proteomics*. Berlin: Springer.

Fayad, J., Russell, C., & Agapito, L. (2011). Automated articulated structure and 3d shape recovery from point correspondences. In *2011 International conference on computer vision*, pp. 431–438. IEEE.

Fuentes-Jiménez, D., Casillas-Pérez, D., Pizarro-Pérez, D., Collins, T., & Bartoli, A. (2018). Deep shape-from-template: Wide-baseline, dense and fast registration and deformable reconstruction from a single image. CoRR abs/1811.07791. arXiv:1811.07791

Golyanik, V., Shimada, S., Varanasi, K., & Stricker, D. (2018). Hdm-net: Monocular non-rigid 3d reconstruction with learned deformation model. In *International conference on virtual reality and augmented reality*, pp. 51–72. Berlin: Springer.

Haouchine, N., Dequidt, J., Berger, M. O., & Cotin, S. (2014). Single view augmentation of 3D elastic objects. In *ISMAR*, pp. 229–236. IEEE.

Hartley, R., & Zisserman, A. (2003). *Multiple view geometry in computer vision*. Cambridge: Cambridge University Press.

Horn, B. K., & Schunck, B. G. (1981). Determining optical flow. *Artificial intelligence*, 17(1–3), 185–203.

Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. *International Journal of Computer Vision*, 60(2), 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94.

Malti, A., Bartoli, A., & Hartley, R. (2015). A linear least-squares solution to elastic shape-from-template. In *CVPR*, pp. 1629–1637.

Malti, A., & Herzet, C. (2017). Elastic shape-from-template with spatially sparse deforming forces. In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pp. 3337–3345.

Ngo, D. T., Östlund, J., & Fua, P. (2016). Template-based monocular 3d shape recovery using Laplacian meshes. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 38(1), 172–187.

Ovsienko, V., & Tabachnikov, S. (2009). What is the schwarzian derivative. *Notices of the AMS*, 56(1), 34–36.

Özgür, E., & Bartoli, A. (2017). Particle-SfT: A provably-convergent, fast shape-from-template algorithm. *International Journal of Computer Vision*, 123(2), 184–205.

Parashar, S., Pizarro, D., & Bartoli, A. (2020). Local deformable 3d reconstruction with cartan's connections. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 42(12), 3011–3026. https://doi.org/10.1109/TPAMI.2019.2920821.

Perriollat, M., Hartley, R., & Bartoli, A. (2011). Monocular template-based reconstruction of inextensible surfaces. *International Journal of Computer Vision*, 95(2), 124–137.

Pilet, J., Lepetit, V., & Fua, P. (2008). Fast non-rigid surface detection, registration and realistic augmentation. *International Journal of Computer Vision*, 76(2), 109–122.

Pizarro, D., & Bartoli, A. (2012). Feature-based deformable surface detection with self-occlusion reasoning. *International Journal of Computer Vision*, 97(1), 54–70.

Pizarro, D., Bartoli, A., & Collins, T. (2013). Isowarp and conwarp: Warps that exactly comply with weak-perspective projection of deforming objects. In *BMVC*.

Pizarro, D., Khan, R., & Bartoli, A. (2016). Schwarps: Locally projective image warps based on 2d schwarzian derivatives. *International Journal of Computer Vision*, 119(2), 93–109. https://doi.org/10.1007/s11263-016-0882-9.

Pumarola, A., Agudo, A., Porzi, L., Sanfeliu, A., Lepetit, V., & Moreno-Noguer, F. (2018). Geometry-aware network for non-rigid shape prediction from a single view. In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pp. 4681–4690.

Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999). Nonrigid registration using free-form deformations: Application to breast MR images. *IEEE Transactions on Medical Imaging*, 18(8), 712–721. https://doi.org/10.1109/42.796284.

Salzmann, M., & Fua, P. (2011). Linear local models for monocular reconstruction of deformable surfaces. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 33(5), 931–944.

Shimada, S., Golyanik, V., Theobalt, C., & Stricker, D. (2019). Ismo-gan: Adversarial learning for monocular non-rigid 3d reconstruction. In *Proceedings of the IEEE conference on computer vision and pattern recognition workshops*.

Sundaram, N., Brox, T., & Keutzer, K. (2010). Dense point trajectories by GPU-accelerated large displacement optical flow. In *European conference on computer vision*, pp. 438–451. Berlin: Springer.
Varol, A., Salzmann, M., Fua, P., & Urtasun, R. (2012). A constrained
latent variable model. In *2012 IEEE conference on computer vision
and pattern recognition*, pp. 2248–2255. IEEE.

Yu, R., Russell, C., Campbell, N. D., & Agapito, L. (2015). Direct, dense, and deformable: Template-based non-rigid 3D reconstruction from RGB video. In *it Proceedings of the IEEE international conference on computer vision*, pp. 918–926.

Zach, C., Pock, T., & Bischof, H. (2007). A duality based approach for realtime tv-11 optical flow. In F. A. Hamprecht, C. Schnörr, & B. Jähne (Eds.), *Pattern recognition* (pp. 214–223). Berlin, Springer.
Varol, A., Salzmann, M., Fua, P., & Urtasun, R. (2012). A constrained
latent variable model. In *2012 IEEE conference on computer vision
and pattern recognition*, pp. 2248–2255. IEEE.

Yu, R., Russell, C., Campbell, N. D., & Agapito, L. (2015). Direct, dense, and deformable: Template-based non-rigid 3D reconstruction from RGB video. In *It Proceedings of the IEEE international conference on computer vision*, pp. 918–926.

Zach, C., Pock, T., & Bischof, H. (2007). A duality based approach for
realtime tv-11 optical flow. In F. A. Hamprecht, C. Schnörr, & B.
Jähne (Eds.), *Pattern recognition* (pp. 214–223). Berlin, Springer.