title: Towards Understanding Camera Motions in Any Video
summary: We introduce CameraBench, a large-scale dataset and benchmark designed to
  assess and improve camera motion understanding. CameraBench consists of ~3,000
  diverse internet videos, annotated by experts through a rigorous multi-stage
  quality control process. One of our contributions is a taxonomy of camera
  motion primitives, designed in collaboration with cinematographers. We find,
  for example, that some motions like "follow" (or tracking) require
  understanding scene content like moving subjects. We conduct a large-scale
  human study to quantify human annotation performance, revealing that domain
  expertise and tutorial-based training can significantly enhance accuracy. For
  example, a novice may confuse zoom-in (a change of intrinsics) with translating
  forward (a change of extrinsics), but can be trained to differentiate the two.
  Using CameraBench, we evaluate Structure-from-Motion (SfM) and Video-Language
  Models (VLMs), finding that SfM models struggle to capture semantic primitives
  that depend on scene content, while VLMs struggle to capture geometric
  primitives that require precise estimation of trajectories. We then fine-tune a
  generative VLM on CameraBench to achieve the best of both worlds and showcase
  its applications, including motion-augmented captioning, video question
  answering, and video-text retrieval. We hope our taxonomy, benchmark, and
  tutorials will drive future efforts towards the ultimate goal of understanding
  camera motions in any video.
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paper_authors.user: Stephen624
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paper_authors.name: Chancharik Mitra
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paper_authors.fullname: Chancharik Mitra
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paper_authors.name: Xue Bai
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paper_authors.name: Chuang Gan
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paper_title: Towards Understanding Camera Motions in Any Video
paper_summary: We introduce CameraBench, a large-scale dataset and benchmark designed to
  assess and improve camera motion understanding. CameraBench consists of ~3,000
  diverse internet videos, annotated by experts through a rigorous multi-stage
  quality control process. One of our contributions is a taxonomy of camera
  motion primitives, designed in collaboration with cinematographers. We find,
  for example, that some motions like "follow" (or tracking) require
  understanding scene content like moving subjects. We conduct a large-scale
  human study to quantify human annotation performance, revealing that domain
  expertise and tutorial-based training can significantly enhance accuracy. For
  example, a novice may confuse zoom-in (a change of intrinsics) with translating
  forward (a change of extrinsics), but can be trained to differentiate the two.
  Using CameraBench, we evaluate Structure-from-Motion (SfM) and Video-Language
  Models (VLMs), finding that SfM models struggle to capture semantic primitives
  that depend on scene content, while VLMs struggle to capture geometric
  primitives that require precise estimation of trajectories. We then fine-tune a
  generative VLM on CameraBench to achieve the best of both worlds and showcase
  its applications, including motion-augmented captioning, video question
  answering, and video-text retrieval. We hope our taxonomy, benchmark, and
  tutorials will drive future efforts towards the ultimate goal of understanding
  camera motions in any video.
paper_ai_summary: CameraBench assesses and enhances camera motion understanding by providing a large-scale dataset, taxonomy, and benchmark for evaluating SfM and VLMs, emphasizing the need for both semantic and geometric information.
paper_ai_keywords: Structure-from-Motion (SfM) Video-Language Models (VLMs) semantic primitives geometric primitives trajectory estimation motion-augmented captioning video question answering video-text retrieval