The translation of images into detailed three-dimensional (3D) models represents a critical challenge in digital content creation, particularly for the Creative Industries (CI). Traditional 3D modeling methods are resource-intensive, while recent advances in Neural Rendering (NR) have introduced efficient and automated solutions. In the fashion industry, where visual fidelity and rapid prototyping are crucial, NR techniques such as Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) are becoming relevant resources to digitize complex geometries and textures with a low-cost approach, enabling different applications like virtual fitting rooms, immersive e-commerce, and digital prototyping. However, the evaluation of their effectiveness in this field is still in its early phases. To address the industry's needs, we propose Fashion immersive Neural Rendering Interface (FENRI), a novel framework that integrates NR techniques for reconstructing 3D models from 2D images of fashion items. FENRI includes a WebXR-based visualization platform that allows immersive comparison and evaluation of NR-generated 3D models, supporting both experts and non-experts in selecting optimal designs. We applied FENRI to footwear design, collecting a novel dataset to compare NeRF and 3DGS methods through quantitative and qualitative analyses. In our study, the 3DGS method demonstrated superior performance over NeRF, as highlighted by the higher Peak Signal-to-Noise Ratio (PSNR) values (37.65 vs. 29.03, respectively) and Structural Similarity Index Measure (SSIM) scores (0.99 vs 0.96), while exhibiting a lower Learned Perceptual Image Patch Similarity (LPIPS) values (0.01 vs 0.04). Moreover, we showed that, by applying classical mesh post-processing techniques, we can increase the topological and visual quality of the 3D models synthesized by NR methods. These findings highlight the potential of NR techniques in the fashion industry's digital pipeline. By enabling rapid, immersive, and visually compelling design iterations, FENRI offers a scalable solution to the fashion industry's demand for high-quality 3D reconstructions, promoting innovation and sustainability.
Balloni, E., Stacchio, L., Mancini, A., Frontoni, E., Zingaretti, P., Paolanti, M. (2025). A Neural Rendering system for fashion design process. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 153, 1-14 [10.1016/j.engappai.2025.110773].
A Neural Rendering system for fashion design process
Stacchio L.;
2025
Abstract
The translation of images into detailed three-dimensional (3D) models represents a critical challenge in digital content creation, particularly for the Creative Industries (CI). Traditional 3D modeling methods are resource-intensive, while recent advances in Neural Rendering (NR) have introduced efficient and automated solutions. In the fashion industry, where visual fidelity and rapid prototyping are crucial, NR techniques such as Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) are becoming relevant resources to digitize complex geometries and textures with a low-cost approach, enabling different applications like virtual fitting rooms, immersive e-commerce, and digital prototyping. However, the evaluation of their effectiveness in this field is still in its early phases. To address the industry's needs, we propose Fashion immersive Neural Rendering Interface (FENRI), a novel framework that integrates NR techniques for reconstructing 3D models from 2D images of fashion items. FENRI includes a WebXR-based visualization platform that allows immersive comparison and evaluation of NR-generated 3D models, supporting both experts and non-experts in selecting optimal designs. We applied FENRI to footwear design, collecting a novel dataset to compare NeRF and 3DGS methods through quantitative and qualitative analyses. In our study, the 3DGS method demonstrated superior performance over NeRF, as highlighted by the higher Peak Signal-to-Noise Ratio (PSNR) values (37.65 vs. 29.03, respectively) and Structural Similarity Index Measure (SSIM) scores (0.99 vs 0.96), while exhibiting a lower Learned Perceptual Image Patch Similarity (LPIPS) values (0.01 vs 0.04). Moreover, we showed that, by applying classical mesh post-processing techniques, we can increase the topological and visual quality of the 3D models synthesized by NR methods. These findings highlight the potential of NR techniques in the fashion industry's digital pipeline. By enabling rapid, immersive, and visually compelling design iterations, FENRI offers a scalable solution to the fashion industry's demand for high-quality 3D reconstructions, promoting innovation and sustainability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


