title: Dynamic Camera Poses and Where to Find Them
summary: Annotating camera poses on dynamic Internet videos at scale is critical for
  advancing fields like realistic video generation and simulation. However,
  collecting such a dataset is difficult, as most Internet videos are unsuitable
  for pose estimation. Furthermore, annotating dynamic Internet videos present
  significant challenges even for state-of-theart methods. In this paper, we
  introduce DynPose-100K, a large-scale dataset of dynamic Internet videos
  annotated with camera poses. Our collection pipeline addresses filtering using
  a carefully combined set of task-specific and generalist models. For pose
  estimation, we combine the latest techniques of point tracking, dynamic
  masking, and structure-from-motion to achieve improvements over the
  state-of-the-art approaches. Our analysis and experiments demonstrate that
  DynPose-100K is both large-scale and diverse across several key attributes,
  opening up avenues for advancements in various downstream applications.
paper_authors._id: 680af390cbada0176f4978ee
paper_authors.name: Chris Rockwell
paper_authors._id: 680af390cbada0176f4978ef
paper_authors.name: Joseph Tung
paper_authors._id: 680af390cbada0176f4978f0
paper_authors.name: Tsung-Yi Lin
paper_authors._id: 680af390cbada0176f4978f1
paper_authors.name: Ming-Yu Liu
paper_authors._id: 680af390cbada0176f4978f2
paper_authors.name: David F. Fouhey
paper_authors._id: 680af390cbada0176f4978f3
paper_authors.name: Chen-Hsuan Lin
paper_title: Dynamic Camera Poses and Where to Find Them
paper_summary: Annotating camera poses on dynamic Internet videos at scale is critical for
  advancing fields like realistic video generation and simulation. However,
  collecting such a dataset is difficult, as most Internet videos are unsuitable
  for pose estimation. Furthermore, annotating dynamic Internet videos present
  significant challenges even for state-of-theart methods. In this paper, we
  introduce DynPose-100K, a large-scale dataset of dynamic Internet videos
  annotated with camera poses. Our collection pipeline addresses filtering using
  a carefully combined set of task-specific and generalist models. For pose
  estimation, we combine the latest techniques of point tracking, dynamic
  masking, and structure-from-motion to achieve improvements over the
  state-of-the-art approaches. Our analysis and experiments demonstrate that
  DynPose-100K is both large-scale and diverse across several key attributes,
  opening up avenues for advancements in various downstream applications.
paper_ai_summary: DynPose-100K is a large-scale dataset of dynamic Internet videos with annotated camera poses, collected using advanced filtering and pose estimation techniques.
paper_ai_keywords: camera poses dynamic Internet videos state-of-the-art methods point tracking dynamic masking structure-from-motion large-scale dataset