Multi-Level Implicit Function for Detailed Human Reconstruction by Relaxing SMPL Constraints

Simple item page
dc.contributor.authorMa, Xikaien_US
dc.contributor.authorZhao, Jieyuen_US
dc.contributor.authorTeng, Yiqingen_US
dc.contributor.authorYao, Lien_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:34:52Z
dc.date.available2023-10-09T07:34:52Z
dc.date.issued2023
dc.description.abstractAiming at enhancing the rationality and robustness of the results of single-view image-based human reconstruction and acquiring richer surface details, we propose a multi-level reconstruction framework based on implicit functions.This framework first utilizes the predicted SMPL model (Skinned Multi-Person Linear Model) as a prior to further predict consistent 2.5D sketches (depth map and normal map), and then obtains a coarse reconstruction result through an Implicit Function fitting network (IF-Net). Subsequently, with a pixel-aligned feature extraction module and a fine IF-Net, the strong constraints imposed by SMPL are relaxed to add more surface details to the reconstruction result and remove noise. Finally, to address the trade-off between surface details and rationality under complex poses, we propose a novel fusion repair algorithm that reuses existing information. This algorithm compensates for the missing parts of the fine reconstruction results with the coarse reconstruction results, leading to a robust, rational, and richly detailed reconstruction. The final experiments prove the effectiveness of our method and demonstrate that it achieves the richest surface details while ensuring rationality. The project website can be found at https://github.com/MXKKK/2.5D-MLIF.en_US
dc.description.number7
dc.description.sectionheadersVirtual Humans
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14951
dc.identifier.issn1467-8659
dc.identifier.pages15 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14951
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14951
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Computer vision; Shape modeling
dc.subjectComputing methodologies
dc.subjectComputer vision
dc.subjectShape modeling
dc.titleMulti-Level Implicit Function for Detailed Human Reconstruction by Relaxing SMPL Constraintsen_US
Files
Original bundle
Now showing 1 - 3 of 3
No Thumbnail Available
Name:
v42i7_23_14951.pdf
Size:
49.91 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
paper1172_mm_1.mp4
Size:
16.46 MB
Format:
Unknown data format
Download
No Thumbnail Available
Name:
paper1172_mm_2.pdf
Size:
2.06 MB
Format:
Adobe Portable Document Format
Download
Collections
42-Issue 7