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The use of autoencoders for shape editing or generation through latent space manipulation suffers from unpredictable changes in the output shape.
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Abdal, R., Zhu, P., Mitra, N.J., Wonka, P.: Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Transactions on Graphics (TOG) 40
2021
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2021
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Chen, Z., Kim, V.G., Fisher, M., Aigerman, N., Zhang, H., Chaudhuri, S.: Decor-gan: 3d shape detailization by conditional refinement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15740–15749 (2021)
2021
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2021
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Li, R., Li, X., Hui, K.H., Fu, C.W.: Sp-gan: sphere-guided 3d shape generation and manipulation. ACM Transactions on Graphics (TOG) 40
2021
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Liu, M., Sung, M., Mech, R., Su, H.: Deepmetahandles: Learning deformation meta-handles of 3d meshes with biharmonic coordinates. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12–21 (2021)
2021
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Uy, M.A., Kim, V.G., Sung, M., Aigerman, N., Chaudhuri, S., Guibas, L.J.: Joint learning of 3d shape retrieval and deformation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11713–11722 (2021)
2021
Closest in time.