This study focuses on multi-human relief modeling using a single photograph.Although previous studies successfully modeled 3D humans from single photographs,they were limited to reconstructing 3D individuals and could...This study focuses on multi-human relief modeling using a single photograph.Although previous studies successfully modeled 3D humans from single photographs,they were limited to reconstructing 3D individuals and could not be applied to multi-human scenes with complex inter-body and outer-body occlusions.In this study,we introduce MMRelief,a novel solution that takes a significant step toward high-quality and generalized multi-human relief modeling.MMRelief uses a three-step approach to achieve its objectives.First,it predicts an occlusion-aware depth map based on ZoeDepth[12].Subsequently,it predicts a detailed normal map using a photo-to-normal network.Finally,MMRelief combines the strengths of both maps and constructs human relief using depth-constrained normal integration.Experimental results demonstrate that MMRelief has achieved state-of-the-art performance in normal human estimation.It can handle different styles of human photos with varying poses and dresses while producing reliefs with accurate body occlusions,reasonable depth ordering,and faithful geometrical details.The project page is at https://github.com/yanqingliu3856/MMRelief.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61772293 and 62072274in part by the Joint Fund of the National Natural Science Foundation of China under Grant No.U22A2033.
文摘This study focuses on multi-human relief modeling using a single photograph.Although previous studies successfully modeled 3D humans from single photographs,they were limited to reconstructing 3D individuals and could not be applied to multi-human scenes with complex inter-body and outer-body occlusions.In this study,we introduce MMRelief,a novel solution that takes a significant step toward high-quality and generalized multi-human relief modeling.MMRelief uses a three-step approach to achieve its objectives.First,it predicts an occlusion-aware depth map based on ZoeDepth[12].Subsequently,it predicts a detailed normal map using a photo-to-normal network.Finally,MMRelief combines the strengths of both maps and constructs human relief using depth-constrained normal integration.Experimental results demonstrate that MMRelief has achieved state-of-the-art performance in normal human estimation.It can handle different styles of human photos with varying poses and dresses while producing reliefs with accurate body occlusions,reasonable depth ordering,and faithful geometrical details.The project page is at https://github.com/yanqingliu3856/MMRelief.
