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Framework for 2D Ad placements in LinearTV

arXiv 2212.02450 ↗First posted 5 Dec 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

Virtual Product placement(VPP) is the advertising technique of digitally placing a branded object into the scene of a movie or TV show. This type of advertising provides the ability for brands to reach consumers without interrupting the viewing experience with a commercial break, as the products are seen in the background or as props. Despite this being a billion-dollar industry, ad rendering technique is currently executed at post production stage, manually either with the help of VFx artists or through semi-automated solutions. In this paper, we demonstrate a fully automated framework to digitally place 2-D ads in linear TV cooking shows captured using single-view camera with small camera movements. Without access to full video or production camera configuration, this framework performs the following tasks (i) identifying empty space for 2-D ad placement (ii) kitchen scene understanding (iii) occlusion handling (iv) ambient lighting and (v) ad tracking.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 50 references that are in the map. First posted on arXiv 5 Dec 2022.

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26 of 50 references.

References · 26

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2017Mask R-CNNcited by 7,259
  3. 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
  4. 2016Semantic Understanding of Scenes through the ADE20K Datasetcited by 1,063
  5. 2021LoFTR: Detector-Free Local Feature Matching with Transformerscited by 974
  6. 2018Path Aggregation Network for Instance Segmentationcited by 796
  7. 2019Panoptic Feature Pyramid Networkscited by 644
  8. 2019YOLACT: Real-time Instance Segmentationcited by 441
  9. 2019Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Imagecited by 202
  10. 2018PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Imagecited by 105
  11. 2019PolyTransform: Deep Polygon Transformer for Instance Segmentationcited by 92
  12. 2019Deep Parametric Indoor Lighting Estimationcited by 82
14 more
  1. 2021Learning Indoor Inverse Rendering with 3D Spatially-Varying Lightingcited by 82
  2. 2018PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Imagecited by 77
  3. 2021Line Segment Detection Using Transformers without Edgescited by 73
  4. 2019AdaptIS: Adaptive Instance Selection Networkcited by 64
  5. 2020Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illuminationcited by 62
  6. 2022IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenescited by 62
  7. 2020Deep Homography Estimation for Dynamic Scenescited by 60
  8. 2019Single-Image Piece-wise Planar 3D Reconstruction via Associative Embeddingcited by 56
  9. 2016Deep Outdoor Illumination Estimationcited by 53
  10. 2022Iterative Deep Homography Estimationcited by 45
  11. 2019All-Weather Deep Outdoor Lighting Estimationcited by 41
  12. 2022Depth-Guided Sparse Structure-from-Motion for Movies and TV Showscited by 15
  13. 2020An Advert Creation System for 3D Product Placementscited by 4
  14. 2018An Advert Creation System for Next-Gen Publicitycited by 2

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