title: Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror
    Reflections
summary: We tackle the problem of generating highly realistic and plausible mirror
  reflections using diffusion-based generative models. We formulate this problem
  as an image inpainting task, allowing for more user control over the placement
  of mirrors during the generation process. To enable this, we create SynMirror,
  a large-scale dataset of diverse synthetic scenes with objects placed in front
  of mirrors. SynMirror contains around 198K samples rendered from 66K unique 3D
  objects, along with their associated depth maps, normal maps and instance-wise
  segmentation masks, to capture relevant geometric properties of the scene.
  Using this dataset, we propose a novel depth-conditioned inpainting method
  called MirrorFusion, which generates high-quality geometrically consistent and
  photo-realistic mirror reflections given an input image and a mask depicting
  the mirror region. MirrorFusion outperforms state-of-the-art methods on
  SynMirror, as demonstrated by extensive quantitative and qualitative analysis.
  To the best of our knowledge, we are the first to successfully tackle the
  challenging problem of generating controlled and faithful mirror reflections of
  an object in a scene using diffusion based models. SynMirror and MirrorFusion
  open up new avenues for image editing and augmented reality applications for
  practitioners and researchers alike.
paper_authors._id: 66f2cd3663244bb5e670e52f
paper_authors.name: Ankit Dhiman
paper_authors._id: 66f2cd3663244bb5e670e530
paper_authors.name: Manan Shah
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-25T07:33:44.279Z
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paper_authors.avatarUrl: /avatars/40f7f4d36727288a69ef7ea94fd3c853.svg
paper_authors.fullname: Manan Shah
paper_authors.type: user
paper_authors.user: cs-mshah
paper_authors._id: 66f2cd3663244bb5e670e531
paper_authors.name: Rishubh Parihar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-09T14:37:30.710Z
paper_authors._id: 631af10b492c0c57a22ea3e2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1662710000523-noauth.png
paper_authors.fullname: Rishubh Parihar
paper_authors.type: user
paper_authors.user: RishubhPar
paper_authors._id: 66f2cd3663244bb5e670e532
paper_authors.name: Yash Bhalgat
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-25T07:33:41.909Z
paper_authors._id: 630918cc3dbb0cf0686bae1d
paper_authors.avatarUrl: /avatars/607cc5ee6a70a283e8d6e2845ce15e41.svg
paper_authors.fullname: Yash Bhalgat
paper_authors.type: user
paper_authors.user: yashbhalgat
paper_authors._id: 66f2cd3663244bb5e670e533
paper_authors.name: Lokesh R Boregowda
paper_authors._id: 66f2cd3663244bb5e670e534
paper_authors.name: R Venkatesh Babu
paper_title: Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror
    Reflections
paper_summary: We tackle the problem of generating highly realistic and plausible mirror
  reflections using diffusion-based generative models. We formulate this problem
  as an image inpainting task, allowing for more user control over the placement
  of mirrors during the generation process. To enable this, we create SynMirror,
  a large-scale dataset of diverse synthetic scenes with objects placed in front
  of mirrors. SynMirror contains around 198K samples rendered from 66K unique 3D
  objects, along with their associated depth maps, normal maps and instance-wise
  segmentation masks, to capture relevant geometric properties of the scene.
  Using this dataset, we propose a novel depth-conditioned inpainting method
  called MirrorFusion, which generates high-quality geometrically consistent and
  photo-realistic mirror reflections given an input image and a mask depicting
  the mirror region. MirrorFusion outperforms state-of-the-art methods on
  SynMirror, as demonstrated by extensive quantitative and qualitative analysis.
  To the best of our knowledge, we are the first to successfully tackle the
  challenging problem of generating controlled and faithful mirror reflections of
  an object in a scene using diffusion based models. SynMirror and MirrorFusion
  open up new avenues for image editing and augmented reality applications for
  practitioners and researchers alike.
paper_ai_summary: A diffusion-based generative model issues high-fidelity mirror reflections using a novel depth-conditioned inpainting method on a large synthetic dataset, enabling user control over mirror placement.
paper_ai_keywords: diffusion-based generative models image inpainting SynMirror depth-conditioned inpainting MirrorFusion geometrically consistent photo-realistic mirror reflections