Shuyang Yu, Haibo Qin, Shaohan Hu, Wei Shan, Xiaoqing Guan, You Wang, Guang Li
Monocular occupancy prediction in indoor scenes aims to recover a scene's 3D geometry and semantics from a single RGB image. Existing methods are restricted to a closed set of predefined categories, limiting their ability to generalize to unseen categories. This limitation is particularly critical in indoor scenes, where objects exhibit highly diverse and fine-grained semantic categories. We study open-vocabulary monocular occupancy prediction for indoor scenes and present OVM-Occ, a Gaussian-based occupancy framework with an instance-aware vision-language teacher. Specifically, we adopt Language-Embedded Gaussians as the intermediate scene representation, propose a geometry-aware deformable attention module to refine Gaussian semantic features, and construct instance-level vision-language embeddings to provide reliable semantic supervision. We further introduce geometry-aware decoupled semantic distillation to mitigate the interference between geometry learning and semantic learning. To evaluate open-vocabulary occupancy prediction performance, we introduce OpenOcc-ScanNet and OpenOcc-Replica, two extended benchmarks with more diverse and fine-grained semantic categories. Experiments show that our method achieves state-of-the-art performance in open-vocabulary monocular occupancy prediction for indoor scenes, outperforming the previous state-of-the-art approach by 12.2 mIoU and 0.6 IoU on the Occ-ScanNet dataset, with consistent gains under new open-vocabulary evaluation protocols.
Recent advances in generative video models have improved visual realism, making detection increasingly dependent on subtle temporal inconsistencies. However, these signals are weak and spatially localized, while most of the video content is redundant. This creates a fundamental mismatch: existing detectors rely on heavy backbones to model full videos, which leads to high computational cost and limited scalability. In this work, we rethink AI-generated video detection from a representation perspective. Instead of modeling entire videos, we restructure them into compact temporal slice representations that isolate informative temporal evolution. We introduce a lightweight framework using frequency-aware temporal slice consistency learning, where informative spatial rows and columns are aggregated over time to form slice-domain inputs. This reparameterization suppresses redundant appearance while preserving discriminative temporal dynamics, enabling efficient and targeted detection. By constructing slices from both high- and low-frequency regions, our method exposes complementary temporal artifacts, including unstable details and abnormal motion smoothness. We instantiate this idea in a Temporal Slice Consistency Network, which integrates task-driven slice localization, hierarchical positional encoding, and a lightweight Transformer to model cross-slice and cross-frequency dependencies. We further introduce AI-Artist, a new benchmark with artist-curated videos from recent high-fidelity generators, including Seedance and Kling. Experiments on GenVideo and AI-Artist show that our method achieves strong performance while requiring over 400$\times$ fewer model-forward FLOPs and 3.6$\times$ faster model inference than the strong video-based baseline ReStraV (2.9$\times$ faster end to end, including preprocessing), enabling scalable and practical deployment.