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Found 6 papers, 2 papers with code
Joint Reconstruction of Spatially-Coherent and Realistic Clothed Humans and Objects from a Single Image
Reconstructing images with humans and objects is challenging due to the occlusions and lack of 3D spatial awareness, which leads to depth ambiguity in the reconstruction.
COSMU: Complete 3D human shape from monocular unconstrained images
The generated 2D normal maps are then processed by a multi-view attention-based neural implicit model that estimates an implicit representation of the 3D shape, ensuring the reproduction of details in both observed and occluded regions.
ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image
In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy.
Super-resolution 3D Human Shape from a Single Low-Resolution Image
The approach overcomes limitations of existing approaches that reconstruct 3D human shape from a single image, which require high-resolution images together with auxiliary data such as surface normal or a parametric model to reconstruct high-detail shape.
Attention-based Multi-Reference Learning for Image Super-Resolution
A novel hierarchical attention-based sampling approach is introduced to learn the similarity between low-resolution image features and multiple reference images based on a perceptual loss.
Super-Resolution Appearance Transfer for 4D Human Performances
Typically the requirement to frame cameras to capture the volume of a dynamic performance ($>50m^3$) results in the person occupying only a small proportion $<$ 10% of the field of view.
