Towards Efficient and Realistic Animation of 3D Garments with Deep Learning
[eng] Machine learning has experienced a soar thanks to the proliferation of deep learning based methodologies. 3D vision is one of the many fields that benefited from this trend. Within this domain, I focused my research on human-centric scenarios. As a starting point, I begin with a 3D human pose...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/202082 |
| Acceso en línea: | https://hdl.handle.net/2445/202082 http://hdl.handle.net/10803/688990 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprenentatge automàtic Xarxes neuronals (Informàtica) Visualització tridimensional Animació per ordinador Indumentària Machine learning Neural networks (Computer science) Three-dimensional display systems Computer animation Costume |
| Sumario: | [eng] Machine learning has experienced a soar thanks to the proliferation of deep learning based methodologies. 3D vision is one of the many fields that benefited from this trend. Within this domain, I focused my research on human-centric scenarios. As a starting point, I begin with a 3D human pose and shape reconstruction approach from still images. Relying on a powerful CNN and a novel inverse graphics solution, I define the steps to predict volumetric humans as 3D meshes. As a natural extension, I turn my attention to the modelling of 3D garments for complete human representation. Deep learning models require huge volumes of data. For this reason, next, I explain my work developing the biggest 3D garment dataset, CLOTH3D. This was motivated by the lack of such data for the study of cloth on humans. Additionally, in the same context, I describe a baseline model for 3D garment generation trained on CLOTH3D. After identification of the major drawbacks of the baseline model, I introduce a novel solution for the garment animation problem. Deep learning models usually require data with a fixed dimensionality. Related works proposed expensive data pre-processings to make data uniform, albeit diminishing the quality, among other issues. By focusing purely in garment animation, I designed a fully-convolutional model that does not suffer from the aforementioned problem. This new model can animate even completely unseen outfits. Nonetheless, cloth animation is a tremendously complex problem. In practice, deep models which encode multiple garments end up showing poor quality. Moreover, I noted significant drawbacks in supervised learning schemes for garments. Motivated by these observations, I devised a novel technique that allows unsupervised training for the 3D garment animation task. As a consequence, this methodology leads to smaller, more robust models that can be obtained in a matter of minutes. Furthermore, it shows an unprecedented level of performance. Because of this, it became the first viable option for deep-based real-time garment animation in real life applications. Nonetheless, it is a quasi-static approach. Cloth dynamics are crucial for proper garment animation. Finally, the last of my contributions describes how to learn cloth dynamics unsupervisedly, making the solution for garment animation complete. Additionally, I establish the bases of this new unsupervised neural garment animation framework. |
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