arXiv cs.AI· Dazhao Du, Shiyan Du, Jian Liu, Yongjian Yu, Bohai Gu, Tao Han, Jie Zhang, Hualuo Liu, Eric Liu, Yujia Zhang, Xi Chen, Song Guo·· 3 小时前AI 评分40
CamChoreo：面向视觉语言模型的时序定位组合式镜头运动理解基准
CamChoreo: Benchmarking Temporally Grounded Compositional Camera Motion Understanding for Vision Language Models
AI 导读
CamChoreo 是首个将镜头运动理解建模为时序定位组合式识别任务的基准，包含 4,229 个单镜头片段、20 个方向感知标签，并配有约 43K 片段的训练语料。
正文 · 原文
Authors:Dazhao Du, Shiyan Du, Jian Liu, Yongjian Yu, Bohai Gu, Tao Han, Jie Zhang, Hualuo Liu, Eric Liu, Yujia Zhang, Xi Chen, Song Guo
Abstract:Understanding camera motion is fundamental to video perception, with applications in spatial intelligence and controllable video generation. Existing camera motion evaluations typically assign labels to entire clips, overlooking within-shot changes and simultaneous movements. We therefore formulate camera motion understanding as temporally grounded, compositional recognition, requiring models to localize motion-consistent intervals and identify all active movements. We introduce CamChoreo, the first benchmark for this task, comprising 4,229 diverse single-shot clips with expert temporal annotations using 20 direction-aware labels. Nearly half of its segments contain compound camera motion. We also construct a separate training corpus of approximately 43K clips with the same annotation format. On CamChoreo, supervised fine-tuning (SFT) on this corpus achieves 62.1/64.1 micro F1 with 4B/8B backbones, outperforming advanced closed-source vision language models (VLMs). To complement the primarily semantic representations of VLM visual encoders with cross-frame geometry, we introduce CamInject, which injects features from a frozen 3D foundation model into the VLM. CamInject improves over SFT but requires running the costly 3D model at inference. We instead propose CamDistill, which distills the same geometric knowledge into lightweight camera tokens during training and removes the 3D model at inference. CamDistill improves micro F1 over same-data SFT by 5.4/3.7 points on 4B/8B backbones, closely approaching CamInject. CamDistill-8B also demonstrates strong zero-shot transfer on the external camera motion understanding benchmarks CameraBench and CameraMotionVQA. Together, these resources and CamDistill enable benchmarking and improving temporally grounded, compositional camera motion understanding.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.10932 [cs.CV] |
| (or arXiv:2608.10932v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.10932 arXiv-issued DOI via DataCite |
Submission history
From: Dazhao Du [view email]
[v1]
Tue, 11 Aug 2026 14:00:02 UTC (16,290 KB)
[v2]
Thu, 12 Nov 2026 07:40:23 UTC (16,733 KB)
来源：arXiv cs.AI · arxiv.org