Neural Shadow Art

• URL: http://arxiv.org/abs/2411.19161v1
• Date: Thu, 28 Nov 2024 14:03:30 GMT
• Title: Neural Shadow Art
• Authors: Caoliwen Wang, Bailin Deng,
• Abstract summary: We introduce Neural Shadow Art, which leverages implicit function representations to expand the possibilities of shadow art. Our method allows projections to match input binary images under various lighting directions and screen orientations. Our approach proves valuable for industrial applications, demonstrating lower material usage and enhanced geometric smoothness.
• Score: 10.23185004100584
• License:
• Abstract: Shadow art is a captivating form of sculptural expression, where the projection of a sculpture in a specific direction reveals a desired shape with high accuracy. In this work, we introduce Neural Shadow Art, which leverages implicit function representations to expand the possibilities of shadow art. Our method provides a more flexible framework that allows projections to match input binary images under various lighting directions and screen orientations, without requiring the light source to be perpendicular to the screen. Unlike previous approaches, our method permits rigid transformations of the projected geometry relative to the input binary image. By optimizing lighting directions and screen orientations simultaneously through the implicit representation of 3D models, we ensure the projection closely resembles the target image. Additionally, like prior works, our method accommodates specific angular constraints, allowing users to fix the projection angle when necessary. Beyond its artistic significance, our approach proves valuable for industrial applications, demonstrating lower material usage and enhanced geometric smoothness. This capability avoids oversimplified results, such as the intersection of cylindrical volumes formed by light rays and the projection image. Furthermore, our approach excels in generating sculptures with complex topologies, surpassing previous methods and achieving sculptural effects akin to those in contemporary art.

Related papers

• Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes [62.769186261245416]
  We present a novel inverse rendering framework for large urban scenes capable of jointly reconstructing the scene geometry, spatially-varying materials, and HDR lighting from a set of posed RGB images with optional depth. Specifically, we use a neural field to account for the primary rays, and use an explicit mesh (reconstructed from the underlying neural field) for modeling secondary rays that produce higher-order lighting effects such as cast shadows.
  arXiv  Detail & Related papers  (2023-04-06T17:51:54Z)
• Designing An Illumination-Aware Network for Deep Image Relighting [69.750906769976]
  We present an Illumination-Aware Network (IAN) which follows the guidance from hierarchical sampling to progressively relight a scene from a single image. In addition, an Illumination-Aware Residual Block (IARB) is designed to approximate the physical rendering process. Experimental results show that our proposed method produces better quantitative and qualitative relighting results than previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-07-21T16:21:24Z)
• DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer [78.91753256634453]
  We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiables. In this work, we propose DIBR++, a hybrid differentiable which supports these effects by combining specularization and ray-tracing. Compared to more advanced physics-based differentiables, DIBR++ is highly performant due to its compact and expressive model.
  arXiv  Detail & Related papers  (2021-10-30T01:59:39Z)
• A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis [163.96778522283967]
  We propose a shading-guided generative implicit model that is able to learn a starkly improved shape representation. An accurate 3D shape should also yield a realistic rendering under different lighting conditions. Our experiments on multiple datasets show that the proposed approach achieves photorealistic 3D-aware image synthesis.
  arXiv  Detail & Related papers  (2021-10-29T10:53:12Z)
• Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting [149.1673041605155]
  We address the problem of jointly estimating albedo, normals, depth and 3D spatially-varying lighting from a single image. Most existing methods formulate the task as image-to-image translation, ignoring the 3D properties of the scene. We propose a unified, learning-based inverse framework that formulates 3D spatially-varying lighting.
  arXiv  Detail & Related papers  (2021-09-13T15:29:03Z)
• Shadow Art Revisited: A Differentiable Rendering Based Approach [26.910401398827123]
  We revisit shadow art using differentiable rendering based optimization frameworks to obtain the 3D sculpture. Our choice of using differentiable rendering for generating shadow art sculptures can be attributed to its ability to learn the underlying 3D geometry solely from image data. We demonstrate the generation of 3D sculptures to cast shadows of faces, animated movie characters, and applicability of the framework to sketch-based 3D reconstruction of underlying shapes.
  arXiv  Detail & Related papers  (2021-07-30T10:43:48Z)
• Learning to Caricature via Semantic Shape Transform [95.25116681761142]
  We propose an algorithm based on a semantic shape transform to produce shape exaggerations. We show that the proposed framework is able to render visually pleasing shape exaggerations while maintaining their facial structures.
  arXiv  Detail & Related papers  (2020-08-12T03:41:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.