This paper presents MonoRelief V2, an end-to-end model designed for directly recovering 2.5D reliefs from single images under complex material and illumination variations. In contrast to its predecessor, MonoRelief V1 (Gao et al. 2025), which was solely trained on synthetic data, MonoRelief V2 incorporates real data to achieve improved robustness, accuracy and efficiency. To overcome the challenge of acquiring large-scale real-world dataset, we generate approximately 15,000 pseudo-real images using a text-to-image generative model, and derive corresponding depth pseudo-labels through fusion of depth and normal predictions. Furthermore, we construct a small-scale real-world dataset (800 samples) via multi-view reconstruction and detail refinement. MonoRelief V2 is then progressively trained on the pseudo-real and real-world datasets. Comprehensive experiments demonstrate its state-of-the-art performance both in depth and normal predictions, highlighting its strong potential for a range of downstream applications.

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.

In this article, we introduce MonoRelief, a novel method that combines the strengths of a depth map and a normal map to achieve high-quality relief recovery from a single image. By constructing a large-scale relief dataset that encompasses a diverse range of relief shapes, materials, and lighting conditions, we enable the training of a robust normal estimation network capable of handling various types of relief images. Furthermore, we leverage the state-of-the-art method, DepthAnything v2 (Yang et al. 2024), to generate depth maps from the input images. By integrating the strengths of both maps, MonoRelief recovers 2.5D reliefs with reasonable depth structures and intricate geometrical details. We validate the effectiveness and robustness of MonoRelief through comprehensive experiments, and showcase its potential in a variety of downstream applications, including Image-to-Relief, Text-to-Relief, Lines-to-Relief and relief reproduction.

This paper aims at extending the method of Zhang et al. (2023) to produce not only portrait bas-reliefs from single photographs, but also high-depth reliefs with reasonable depth ordering. We cast this task as a problem of style-aware photo-to-depth translation, where the input is a photograph conditioned by a style vector and the output is a portrait relief with desired depth style. To construct ground-truth data for network training, we first propose an optimization-based method to synthesize high-depth reliefs from 3D portraits. Then, we train a normal-to-depth network to learn the mapping from normal maps to relief depths. After that, we use the trained network to generate high-depth relief samples using the provided normal maps from Zhang et al. (2023). As each normal map has pixel-wise photograph, we are able to establish correspondences between photographs and high-depth reliefs. By taking the bas-reliefs of Zhang et al. (2023), the new high-depth reliefs and their mixtures as target ground-truths, we finally train a encoder-to-decoder network to achieve style-aware relief modeling. Specially, the network is based on a U-shaped architecture, consisting of Swin Transformer blocks to process hierarchical deep features. Extensive experiments have demonstrated the effectiveness of the proposed method. Comparisons with previous works have verified its flexibility and state-of-the-art performance.

In this paper, we present an end-to-end neural solution to model portrait bas-relief from a single photograph, which is cast as a problem of image-to-depth translation. The main challenge is the lack of bas-relief data for network training. To solve this problem, we propose a semi-automatic pipeline to synthesize bas-relief samples. The main idea is to first construct normal maps from photos, and then generate bas-relief samples by reconstructing pixel-wise depths. In total, our synthetic dataset contains 23 k pixel-wise photo/bas-relief pairs. Since the process of bas-relief synthesis requires a certain amount of user interactions, we propose end-to-end solutions with various network architectures, and train them on the synthetic data. We select the one that gave the best results through qualitative and quantitative comparisons. Experiments on numerous portrait photos, comparisons with state-of-the-art methods and evaluations by artists have proven the effectiveness and efficiency of the selected network.

Abstract Different from other types of bas‐reliefs, a flower bas‐relief contains a large number of depth‐discontinuity edges. Most existing line‐based methods reconstruct free‐form surfaces by ignoring the depth‐discontinuities, thus are less efficient in modeling flower bas‐reliefs. This paper presents a neural‐based solution which benefits from the recent advances in CNN. Specially, we use line gradients to encode the depth orderings at leaf edges. Given a line drawing, a heuristic method is first proposed to compute 2D gradients at lines. Line gradients and dense curvatures interpolated from sparse user inputs are then fed into a neural network, which outputs depths and normals of the final bas‐relief. In addition, we introduce an object‐based method to generate flower bas‐reliefs and line drawings for network training. Extensive experiments show that our method is effective in modelling bas‐reliefs with depth‐discontinuity edges. User evaluation also shows that our method is intuitive and accessible to common users.

Abstract Grayscale images are intensively used to construct or represent geometric details infield of computer graphics. In practice, displacement mapping technique often allows an 8‐bit grayscale image input to manipulate the position of vertices. Human eyes are insensitive to the change of intensity between consecutive gray levels, so a grayscale image only provides 256 levels of luminances. However, when the luminances are converted into geometric elements, certain artifacts such as false contours become obvious. In this paper, we formulate the geometric decontouring as a constrained optimization problem from a geometric perspective. Instead of directly solving this optimization problem, we propose a data‐driven method to learn a residual mapping function. We design a Geometric DeContouring Network (GDCNet) to eliminate the false contours effectively. To this end, we adopt a ResNet‐based network structure and a normal‐based loss function. Extensive experimental results demonstrate that accurate reconstructions can be achieved effectively. Our method can be used as a relief compressed representation and enhance the traditional displacement mapping technique to augment 3D models with high‐quality geometric details using grayscale images efficiently.

Abstract In contrast to 3D model that can be freely observed, p ortrait bas‐relief projects slightly from the background and is limited by fixed viewpoint. In this paper, we propose a novel method to reconstruct the underlying 3D shape from a single 2.5D bas‐relief, providing observers wider viewing perspectives. Our target is to make the reconstructed portrait has natural depth ordering and similar appearance to the input. To achieve this, we first use a 3D template face to fit the portrait. Then, we optimize the face shape by normal transfer and Poisson surface reconstruction. The hair and body regions are finally reconstructed and combined with the 3D face. From the resulting 3D shape, one can generate new reliefs with varying poses and thickness, freeing the input one from fixed view. A number of experimental results verify the effectiveness of our method.

We present a novel solution to enable portrait relief modeling from a single image. The main challenges are geometry reconstruction, facial details recovery and depth structure preservation. Previous image-based methods are developed for portrait bas-relief modeling in 2.5D form, but not adequate for 3D-like high relief modeling with undercut features. In this paper, we propose a template-based framework to generate portrait reliefs of various forms. Our method benefits from Shape-from-Shading (SFS). Specifically, we use bi-Laplacian mesh deformation to guide the relief modeling. Given a portrait image, we first use a template face to fit the portrait. We then apply bi-Laplacian mesh deformation to align the facial features. Afterwards, SFS-based reconstruction with a few user interactions is used to optimize the face depth, and create a relief with similar appearance to the input. Both depth structures and geometric details can be well constructed in the final relief. Experiments and comparisons to other methods demonstrate the effectiveness of the proposed method.

Abstract As an art form between drawing and sculpture, relief has been widely used in a variety of media for signs, narratives, decorations and other purposes. Traditional relief creation relies on both professional skills and artistic expertise, which is extremely time‐consuming. Recently, automatic or semi‐automatic relief modelling from a 3D object or a 2D image has been a subject of interest in computer graphics. Various methods have been proposed to generate reliefs with few user interactions or minor human efforts, while preserving or enhancing the appearance of the input. This survey provides a comprehensive review of the advances in computer‐assisted relief modelling during the past decade. First, we provide an overview of relief types and their art characteristics. Then, we introduce the key techniques of object‐space methods and image‐space methods respectively. Advantages and limitations of each category are discussed in details. We conclude the report by discussing directions for possible future research.

Abstract Bas‐relief is designed to provide 3D perception for the viewers under illumination. For the problem of bas‐relief generation from 3D object, most existing methods ignore the influence of illumination on bas‐relief appearance. In this paper, we propose a novel method that adaptively generate bas‐reliefs with respect to illumination conditions. Given a 3D object and its target appearance, our method finds an adaptive surface that preserves the appearance of the input. We validate our approach through a variety of applications. Experimental results indicate that the proposed approach is effective in producing bas‐reliefs with desired appearance under illumination.

In this paper, we introduce a novel approach to bas-relief generation and shape editing that uses gradient-based mesh deformation as the theoretical foundation. Our approach differs from image-based methods in that it operates directly on the triangular mesh, and ensures that the mesh topology remains unchanged during geometric processing. By implicitly deforming the input mesh through gradient manipulation, our approach is applicable to both plane surface bas-relief generation and curved surface bas-relief generation. We propose a series of gradient-based algorithms, such as height field deformation, high slope optimization, fine detail preservation, curved surface flattening and relief mapping. Additionally, we present two types of shape editing tools that allow the user to interactively modify the bas-relief to exhibit a desired shape. Experimental results indicate that the proposed approach is effective in producing plausible and impressive bas-reliefs.