{"ID":2836259,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21098","arxiv_id":"2511.21098","title":"Pygmalion Effect in Vision: Image-to-Clay Translation for Reflective Geometry Reconstruction","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.","short_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 im...","url_abs":"https://arxiv.org/abs/2511.21098","url_pdf":"https://arxiv.org/pdf/2511.21098v1","authors":"[\"Gayoung Lee\",\"Junho Kim\",\"Jin-Hwa Kim\",\"Junmo Kim\"]","published":"2025-11-26T06:34:58Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.GR\"]","methods":"[]","has_code":false}