Data-driven models of 3D avatars and clothing for virtual try-on
| dc.contributor.author | Santesteban, Igor | |
| dc.date.accessioned | 2023-01-15T17:19:15Z | |
| dc.date.available | 2023-01-15T17:19:15Z | |
| dc.date.issued | 2022-07 | |
| dc.description.abstract | Clothing plays a fundamental role in our everyday lives. When we choose clothing to buy or wear, we guide our decisions based on a combination of fit and style. For this reason, the majority of clothing is purchased at brick-and-mortar retail stores, after physical try-on to test the fit and style of several garments on our own bodies. Computer graphics technology promises an opportunity to support online shopping through virtual try-on, but to date virtual try-on solutions lack the responsiveness of a physical try-on experience. This thesis works towards developing new virtual try-on solutions that meet the demanding requirements of accuracy, interactivity and scalability. To this end, we propose novel data-driven models for 3D avatars and clothing that produce highly realistic results at a fraction of the computational cost of physics-based approaches. Throughout the thesis we also address common limitations of data-driven methods by using self-supervision mechanisms to enforce physical constraints and reduce the dependency on ground-truth data. This allows us to build efficient and accurate models with minimal preprocessing times. | en_US |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/2633267 | |
| dc.language.iso | en | en_US |
| dc.subject | virtual try-on | en_US |
| dc.subject | soft-tissue animation | en_US |
| dc.subject | clothing animation | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | human avatars | en_US |
| dc.title | Data-driven models of 3D avatars and clothing for virtual try-on | en_US |
| dc.type | Thesis | en_US |
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