OccJEPA: Occlusion as Masking for Amodal Video Segmentation

Amodal segmentation is a challenging task that aims to predict the complete geometric shape of objects, including their occluded regions. Although existing methods primarily focus on amodal segmentation within the training domain, these approaches often lack the generalization capacity to extend effectively to novel object categories and unseen contexts. This paper introduces Amodal SAM, a unified framework that leverages SAM (Segment Anything Model) for both amodal image and amodal video segmentation. Amodal SAM preserves the powerful generalization ability of SAM while extending its inherent capabilities to the amodal segmentation task. The improvements lie in three aspects: (1) a lightweight Spatial Completion Adapter that enables occluded region reconstruction, (2) a Target-Aware Occlusion Synthesis (TAOS) pipeline that addresses the scarcity of amodal annotations by generating diverse synthetic training data, and (3) novel learning objectives that enforce regional consistency and topological regularization. Extensive experiments demonstrate that Amodal SAM achieves state-of-the-art performance on standard benchmarks, while simultaneously exhibiting robust generalization to novel scenarios. We anticipate that this research will advance the field toward practical amodal segmentation systems capable of operating effectively in unconstrained real-world environments.

Self-supervised image encoders deliver strong features for downstream tasks but need many images for training. A natural remedy to counter this is to make each image count for more. A scene contains many objects, and given masks from human annotators or an off-the-shelf segmentation model, pre-training can focus on aligning per-object rather than image-wide representations, extracting more signal from every image. Existing mask-supervised methods do this through reconstruction or contrastive losses that leverage negative objects. We instead use two separate projection spaces for the alignment. In a semantic space, per-object representations from different views are aligned. To avoid collapse, instead of using negative objects, which requires category definitions, we extend the negative-free LeJEPA objective and show that its distributional anti-collapse regularizer ports naturally from whole images to the variable-sized set of objects in a scene. In an instance space, a contrastive loss separates per-object representations from their context and co-occurring instances, including those of the same category. To separate object representations from their context, we copy objects and paste them into other contexts, where each pasted copy serves as an additional view of the original object. Trained on COCO with ground-truth masks, our method outperforms image-level and mask-guided baselines on tracking (DAVIS), classification (ImageNet-1k) and re-identification (NAVI), matches the best of them on semantic segmentation (ADE20k) and keeps its lead over image-level LeJEPA and a supervision-matched alternative on COCO fractions down to 256 images.

Self-supervised video models are increasingly framed as world models, yet they are still evaluated almost entirely on clean video and reported as a final task score, obscuring how their representations behave under the degraded and ambiguous conditions a deployed world model must handle. We present the first systematic study of this component, analyzing four matched-capacity frontier self-supervised learning models that are strong candidates for world-model encoders -- V-JEPA 2.1, V-JEPA 2, VideoPrism, and VideoMAEv2 -- across five robustness axes relevant to their deployment as video world models: feature discriminability, corruption robustness, fine-grained discrimination, occlusion robustness, and sensitivity to temporal direction. We run the study on Something-Something-v2 and repeat every axis that ports on the egocentric EGTEA Gaze+, where the model ordering is unchanged. Our results reveal a distinct and consistent profile for latent-prediction models across all five axes. They degrade more gracefully under pixel corruption, preserve usable class structure rather than mere geometric stability under occlusion, capture fine-grained physical contact cues without reconstructing pixels, and uniquely encode the arrow of time. We finally test whether these representation-level differences matter for downstream world modeling, pairing each frozen encoder with an identical action-conditioned predictor and planner in a simulated manipulation environment. Only latent prediction yields near-complete task success, remains effective under degraded observations, and transfers to a manipulation task for which the predictor was never trained. Our results provide concrete new evidence that latent prediction is a promising foundation for robust world modeling.