CamReasoner: Reinforcing Camera Movement Understanding via Structured Spatial Reasoning

2026-01-30

AI Summary

This work addresses the limitations of existing camera motion understanding methods, which often treat the problem as a black-box classification task, failing to distinguish physically distinct motion types and neglecting crucial geometric cues. To overcome this, the authors propose a structured reasoning framework inspired by the “Observe–Think–Answer” (O-T-A) paradigm that explicitly models spatiotemporal trajectories and view frustum geometry in videos, enabling physically consistent motion interpretation. For the first time in this domain, reinforcement learning is introduced to align logical reasoning with geometric constraints. The study also presents a large-scale dataset comprising 18k supervised fine-tuning (SFT) reasoning chains and 38k reinforcement learning feedback samples. The proposed method significantly mitigates hallucination and achieves state-of-the-art performance across multiple benchmarks, advancing camera motion understanding from perceptual recognition toward cinematic-level spatial reasoning.

Technology Category

• Computer Vision: Motion & Tracking
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Understanding camera dynamics is a fundamental pillar of video spatial intelligence. However, existing multimodal models predominantly treat this task as a black-box classification, often confusing physically distinct motions by relying on superficial visual patterns rather than geometric cues. We present CamReasoner, a framework that reformulates camera movement understanding as a structured inference process to bridge the gap between perception and cinematic logic. Our approach centers on the Observation-Thinking-Answer (O-T-A) paradigm, which compels the model to decode spatio-temporal cues such as trajectories and view frustums within an explicit reasoning block. To instill this capability, we construct a Large-scale Inference Trajectory Suite comprising 18k SFT reasoning chains and 38k RL feedback samples. Notably, we are the first to employ RL for logical alignment in this domain, ensuring motion inferences are grounded in physical geometry rather than contextual guesswork. By applying Reinforcement Learning to the Observation-Think-Answer (O-T-A) reasoning paradigm, CamReasoner effectively suppresses hallucinations and achieves state-of-the-art performance across multiple benchmarks.

Problem

• camera movement understanding
• spatial reasoning
• multimodal models
• geometric cues
• video spatial intelligence

Innovation

• structured spatial reasoning
• camera movement understanding
• reinforcement learning
• Observation-Thinking-Answer paradigm
• geometric grounding