Pygmalion Effect in Vision: Image-to-Clay Translation for Reflective Geometry Reconstruction
- URL: http://arxiv.org/abs/2511.21098v1
- Date: Wed, 26 Nov 2025 06:34:58 GMT
- Title: Pygmalion Effect in Vision: Image-to-Clay Translation for Reflective Geometry Reconstruction
- Authors: Gayoung Lee, Junho Kim, Jin-Hwa Kim, Junmo Kim,
- Abstract summary: Pygmalion Effect in Vision is a novel framework that metaphorically "sculpts" reflective objects into clay-like forms through image-to-clay translation.<n>Inspired by the myth of Pygmalion, our method learns to suppress specular cues while preserving intrinsic geometric consistency.
- Score: 34.261318579652816
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding reflection remains a long-standing challenge in 3D reconstruction due to the entanglement of appearance and geometry under view-dependent reflections. In this work, we present the Pygmalion Effect in Vision, a novel framework that metaphorically "sculpts" reflective objects into clay-like forms through image-to-clay translation. Inspired by the myth of Pygmalion, our method learns to suppress specular cues while preserving intrinsic geometric consistency, enabling robust reconstruction from multi-view images containing complex reflections. Specifically, we introduce a dual-branch network in which a BRDF-based reflective branch is complemented by a clay-guided branch that stabilizes geometry and refines surface normals. The two branches are trained jointly using the synthesized clay-like images, which provide a neutral, reflection-free supervision signal that complements the reflective views. Experiments on both synthetic and real datasets demonstrate substantial improvement in normal accuracy and mesh completeness over existing reflection-handling methods. Beyond technical gains, our framework reveals that seeing by unshining, translating radiance into neutrality, can serve as a powerful inductive bias for reflective object geometry learning.
Related papers
- MaterialRefGS: Reflective Gaussian Splatting with Multi-view Consistent Material Inference [83.38607296779423]
We show that multi-view consistent material inference with more physically-based environment modeling is key to learning accurate reflections with Gaussian Splatting.<n>Our method faithfully recovers both illumination and geometry, achieving state-of-the-art rendering quality in novel views synthesis.
arXiv Detail & Related papers (2025-10-13T13:29:20Z) - Reflect3r: Single-View 3D Stereo Reconstruction Aided by Mirror Reflections [55.248092751290834]
Mirror reflections are common in everyday environments and can provide stereo information within a single capture.<n>We exploit this property by treating the reflection as an auxiliary view and designing a transformation that constructs a physically valid virtual camera.<n>This enables a multi-view stereo setup from a single image, simplifying the imaging process.
arXiv Detail & Related papers (2025-09-24T23:00:22Z) - Reflections Unlock: Geometry-Aware Reflection Disentanglement in 3D Gaussian Splatting for Photorealistic Scenes Rendering [51.223347330075576]
Ref-Unlock is a novel geometry-aware reflection modeling framework based on 3D Gaussian Splatting.<n>Our approach employs a dual-branch representation with high-order spherical harmonics to capture high-frequency reflective details.<n>Our method thus offers an efficient and generalizable solution for realistic rendering of reflective scenes.
arXiv Detail & Related papers (2025-07-08T15:45:08Z) - NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images [62.752710734332894]
NeRSP is a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images.
We derive photometric and geometric cues from the polarimetric image formation model and multiview azimuth consistency.
We achieve the state-of-the-art surface reconstruction results with only 6 views as input.
arXiv Detail & Related papers (2024-06-11T09:53:18Z) - SFDM: Robust Decomposition of Geometry and Reflectance for Realistic Face Rendering from Sparse-view Images [11.937182322353621]
We introduce a novel two-stage technique for decomposing and reconstructing facial features from sparse-view images.<n>We endeavor to decouple key facial attributes from the RGB color, including geometry, diffuse reflectance, and specular reflectance.
arXiv Detail & Related papers (2023-12-11T03:14:58Z) - DeepShaRM: Multi-View Shape and Reflectance Map Recovery Under Unknown
Lighting [35.18426818323455]
We derive a novel multi-view method, DeepShaRM, that achieves state-of-the-art accuracy on this challenging task.
We introduce a novel deep reflectance map estimation network that recovers the camera-view reflectance maps.
A deep shape-from-shading network then updates the geometry estimate expressed with a signed distance function.
arXiv Detail & Related papers (2023-10-26T17:50:10Z) - DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow
Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering [75.86523223933912]
Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light.
We propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling.
Our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths.
arXiv Detail & Related papers (2023-03-27T11:10:54Z) - Monocular Reconstruction of Neural Face Reflectance Fields [0.0]
The reflectance field of a face describes the reflectance properties responsible for complex lighting effects.
Most existing methods for estimating the face reflectance from a monocular image assume faces to be diffuse with very few approaches adding a specular component.
We present a new neural representation for face reflectance where we can estimate all components of the reflectance responsible for the final appearance from a single monocular image.
arXiv Detail & Related papers (2020-08-24T08:19:05Z) - Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images [59.906948203578544]
We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object.
We first estimate per-view depth maps using a deep multi-view stereo network.
These depth maps are used to coarsely align the different views.
We propose a novel multi-view reflectance estimation network architecture.
arXiv Detail & Related papers (2020-03-27T21:28:54Z)
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.