Human Image Transfer: From Natural Settings to Controlled Studios
| dc.contributor.author | NORDIN, ISAC | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för fysik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Physics | en |
| dc.contributor.examiner | Volpe, Giovanni | |
| dc.contributor.supervisor | Vedig, Fredrik | |
| dc.date.accessioned | 2023-06-27T10:35:30Z | |
| dc.date.available | 2023-06-27T10:35:30Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2023 | |
| dc.description.abstract | Machine learning (ML) and artificial intelligence (AI) are two concepts that have driven advancements in various fields, including the fashion industry. Virtual try on, the technology which allows a user to change a person’s clothes in an image, has gained more attention. However, their potential cannot be fully utilized when applied to photos captured in real-world environments (in-the-wild). Therefore, this project aims to investigate methods for realistically transferring in-the-wild photos to in-studio photos, focusing on domain transfer models. To do this, four CNN-based models were used, trained to fix augmented in-studio images to their original state. The augmentations attempted to simulate in-the-wild images by swapping the background and making the image brighter and blurrier among others. To help the model discern the background of the person a segmentation extractor was used and evaluated. To get the final model, various experiments were done. The model demonstrated its ability to remove lighting, fix sharpened images, and remove noise, but failed at removing shadows among other things. The model showcased better performance at transferring in-the-wild images to in-studio images than copy-pasting the person into a studio background. The segmentation played an important role, ignoring to include body parts that were not inside the segmentation. The evaluation method showcased inconsistencies and needs further research. | |
| dc.identifier.coursecode | TIFX05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/306443 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | Deep learning, Domain transfer, Virtual Try on | |
| dc.title | Human Image Transfer: From Natural Settings to Controlled Studios | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Complex adaptive systems (MPCAS), MSc |