arXiv:2504.15376v1 Announce Type: cross
Abstract: 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.
CameraBench: A Step Towards Understanding Camera Motion in Videos
In the world of multimedia information systems, understanding camera motion in videos is a crucial task. It has applications in various domains such as animations, artificial reality, augmented reality, and virtual realities. To improve camera motion understanding, a team of researchers has introduced CameraBench, a large-scale dataset and benchmark.
CameraBench comprises approximately 3,000 diverse internet videos that have been annotated by experts using a rigorous multi-stage quality control process. This dataset presents a significant contribution to the field, as it provides a valuable resource for assessing and improving camera motion understanding algorithms.
One key aspect of CameraBench is the collaboration with cinematographers, which has led to the development of a taxonomy of camera motion primitives. This taxonomy helps classify different types of camera motions and their dependencies on scene content. For example, a camera motion like “follow” requires understanding of moving subjects in the scene.
To evaluate human annotation performance, a large-scale human study was conducted. The results showed that domain expertise and tutorial-based training significantly enhance accuracy. Novices may initially struggle with differentiating between camera motions like zoom-in (a change of intrinsics) and translating forward (a change of extrinsics). However, through training, they can learn to differentiate between these motions.
The researchers also evaluated Structure-from-Motion (SfM) models and Video-Language Models (VLMs) using CameraBench. They found that SfM models struggle to capture semantic primitives that depend on scene content, while VLMs struggle with geometric primitives that require precise estimation of trajectories. To address these limitations, a generative VLM was fine-tuned with CameraBench to achieve a hybrid model that combines the strengths of both approaches.
This hybrid model opens up a range of applications, including motion-augmented captioning, video question answering, and video-text retrieval. By better understanding camera motions in videos, these applications can be enhanced, providing more immersive experiences for users.
The taxonomy, benchmark, and tutorials provided with CameraBench are valuable resources for researchers and practitioners working towards the ultimate goal of understanding camera motions in any video. The multi-disciplinary nature of camera motion understanding makes it relevant to various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.