title: Dynamic NeRFs for Soccer Scenes
summary: The long-standing problem of novel view synthesis has many applications,
  notably in sports broadcasting. Photorealistic novel view synthesis of soccer
  actions, in particular, is of enormous interest to the broadcast industry. Yet
  only a few industrial solutions have been proposed, and even fewer that achieve
  near-broadcast quality of the synthetic replays. Except for their setup of
  multiple static cameras around the playfield, the best proprietary systems
  disclose close to no information about their inner workings. Leveraging
  multiple static cameras for such a task indeed presents a challenge rarely
  tackled in the literature, for a lack of public datasets: the reconstruction of
  a large-scale, mostly static environment, with small, fast-moving elements.
  Recently, the emergence of neural radiance fields has induced stunning progress
  in many novel view synthesis applications, leveraging deep learning principles
  to produce photorealistic results in the most challenging settings. In this
  work, we investigate the feasibility of basing a solution to the task on
  dynamic NeRFs, i.e., neural models purposed to reconstruct general dynamic
  content. We compose synthetic soccer environments and conduct multiple
  experiments using them, identifying key components that help reconstruct soccer
  scenes with dynamic NeRFs. We show that, although this approach cannot fully
  meet the quality requirements for the target application, it suggests promising
  avenues toward a cost-efficient, automatic solution. We also make our work
  dataset and code publicly available, with the goal to encourage further efforts
  from the research community on the task of novel view synthesis for dynamic
  soccer scenes. For code, data, and video results, please see
  https://soccernerfs.isach.be.
paper_authors._id: 65029e427b875426c918a24f
paper_authors.name: Sacha Lewin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-14T07:42:50.811Z
paper_authors._id: 633df08b55a63a501a4e264b
paper_authors.avatarUrl: /avatars/9406b461fa633c4eafdb335cb7eef6a5.svg
paper_authors.fullname: Sacha lewin
paper_authors.type: user
paper_authors.user: iSach
paper_authors._id: 65029e427b875426c918a250
paper_authors.name: Maxime Vandegar
paper_authors._id: 65029e427b875426c918a251
paper_authors.name: Thomas Hoyoux
paper_authors._id: 65029e427b875426c918a252
paper_authors.name: Olivier Barnich
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-14T07:43:10.997Z
paper_authors._id: 6311e8b7ae8896941da3bdd1
paper_authors.avatarUrl: /avatars/86c72945efa3bf6f291ad87b3649e778.svg
paper_authors.fullname: Olivier Barnich
paper_authors.type: user
paper_authors.user: olivierbarnich
paper_authors._id: 65029e427b875426c918a253
paper_authors.name: Gilles Louppe
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-09-14T11:03:40.183Z
paper_authors._id: 64103068928400b41642ad8a
paper_authors.avatarUrl: /avatars/8acbe90178973081ecd1bb386e1646b5.svg
paper_authors.fullname: Gilles Louppe
paper_authors.type: user
paper_authors.user: glouppe
paper_title: Dynamic NeRFs for Soccer Scenes
paper_summary: The long-standing problem of novel view synthesis has many applications,
  notably in sports broadcasting. Photorealistic novel view synthesis of soccer
  actions, in particular, is of enormous interest to the broadcast industry. Yet
  only a few industrial solutions have been proposed, and even fewer that achieve
  near-broadcast quality of the synthetic replays. Except for their setup of
  multiple static cameras around the playfield, the best proprietary systems
  disclose close to no information about their inner workings. Leveraging
  multiple static cameras for such a task indeed presents a challenge rarely
  tackled in the literature, for a lack of public datasets: the reconstruction of
  a large-scale, mostly static environment, with small, fast-moving elements.
  Recently, the emergence of neural radiance fields has induced stunning progress
  in many novel view synthesis applications, leveraging deep learning principles
  to produce photorealistic results in the most challenging settings. In this
  work, we investigate the feasibility of basing a solution to the task on
  dynamic NeRFs, i.e., neural models purposed to reconstruct general dynamic
  content. We compose synthetic soccer environments and conduct multiple
  experiments using them, identifying key components that help reconstruct soccer
  scenes with dynamic NeRFs. We show that, although this approach cannot fully
  meet the quality requirements for the target application, it suggests promising
  avenues toward a cost-efficient, automatic solution. We also make our work
  dataset and code publicly available, with the goal to encourage further efforts
  from the research community on the task of novel view synthesis for dynamic
  soccer scenes. For code, data, and video results, please see
  https://soccernerfs.isach.be.
paper_ai_keywords: neural radiance fields NeRFs dynamic NeRFs neural models novel view synthesis photorealistic results large-scale, mostly static environment small, fast-moving elements synthetic soccer environments reconstruction soccer SCENES