Version 1
: Received: 17 August 2024 / Approved: 19 August 2024 / Online: 20 August 2024 (10:50:07 CEST)
How to cite:
Shahzad, K.; Naseer, A.; Wali, A.; Tamoor, M. A Novel Technique for Optimizing Volumetric Avatars: From 2D Images to Lightweight 3D Models. Preprints2024, 2024081391. https://doi.org/10.20944/preprints202408.1391.v1
Shahzad, K.; Naseer, A.; Wali, A.; Tamoor, M. A Novel Technique for Optimizing Volumetric Avatars: From 2D Images to Lightweight 3D Models. Preprints 2024, 2024081391. https://doi.org/10.20944/preprints202408.1391.v1
Shahzad, K.; Naseer, A.; Wali, A.; Tamoor, M. A Novel Technique for Optimizing Volumetric Avatars: From 2D Images to Lightweight 3D Models. Preprints2024, 2024081391. https://doi.org/10.20944/preprints202408.1391.v1
APA Style
Shahzad, K., Naseer, A., Wali, A., & Tamoor, M. (2024). A Novel Technique for Optimizing Volumetric Avatars: From 2D Images to Lightweight 3D Models. Preprints. https://doi.org/10.20944/preprints202408.1391.v1
Chicago/Turabian Style
Shahzad, K., Aamir Wali and Maria Tamoor. 2024 "A Novel Technique for Optimizing Volumetric Avatars: From 2D Images to Lightweight 3D Models" Preprints. https://doi.org/10.20944/preprints202408.1391.v1
Abstract
In the world of digital avatars, three-dimensional (3D) human models are utilized to replicate real-world appearance and movements with a higher degree of realism compared to traditional 2D representations. Currently, extensive person-specific data capturing and 3D artists are required to create photorealistic avatars of existing people. Determining the 3D pose and articulation of an avatar from 2D images requires complex algorithms, a capability commonly found in the Visual Effects (VFX) industry but not readily available elsewhere. Hence, this study proposes an approach to generate 3D avatar from a 2D image of a person with uncanny resemblance and an accurate depiction of the subject’s likeness. The proposed approach, PIFu+CycleGAN, combines Pixel-aligned Implicit Function (PIFu) and cycleGAN for textured avatar construction. PIFu is specifically used to capture intricate details and arbitrary topology. Additionally, the hourglass architecture-based module is utilized for T-pose estimation for predicting the initial geometry and shape. Evaluating the proposed approach on the benchmark RenderPeople dataset, it outperforms the state-of-the-art models with values of 1.53 and 1.50 for the Chamfer and P2S distance, respectively. This indicates the creation of high-quality 3D meshes with promising textures, which are of exceptional quality and suitable for animation.
Keywords
3D; avatars; PIFu; CycleGAN; Generative AI; CNN
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
