3DMoC: Training-Free AI-Generated Video Detection via 3D Motion Consistency

Existing methods for detecting AI-generated videos can identify common 2D spatiotemporal anomalies. However, there remain two limitations for detecting high-quality videos: (1) These methods primarily detect local pixel artifacts but lack spatial modeling, making it difficult to identify appearance differences from different views. (2) They primarily measure temporal inconsistencies based on inter-frame differences, thus struggling to identify motion anomalies in space. To address the above issues, we propose a training-free generated video detection via 3D Motion Consistency (3DMoC), which captures 3D appearance and motion anomalies in video, thereby achieving more accurate identification of AI-generated videos. Specifically, to characterize the appearance consistency of elements, we devise a Tracked Appearance Consistency module that aligns the same region across views through trajectory tracking to capture appearance differences in scene. Furthermore, we design a 3D Motion Continuity module that measures the degree of motion variation between tracked 3D world points to identify motion anomalies. Benefiting from the collaborative designs, our 3DMoC effectively captures both anomalies in 3D appearance and motion within generated videos. Extensive experiments demonstrate that our method achieves state-of-the-art detection accuracy on different generated-video benchmarks.