Using Diffusion Priors for Video Amodal Segmentation (2412.04623v1)

Abstract: Object permanence in humans is a fundamental cue that helps in understanding persistence of objects, even when they are fully occluded in the scene. Present day methods in object segmentation do not account for this amodal nature of the world, and only work for segmentation of visible or modal objects. Few amodal methods exist; single-image segmentation methods cannot handle high-levels of occlusions which are better inferred using temporal information, and multi-frame methods have focused solely on segmenting rigid objects. To this end, we propose to tackle video amodal segmentation by formulating it as a conditional generation task, capitalizing on the foundational knowledge in video generative models. Our method is simple; we repurpose these models to condition on a sequence of modal mask frames of an object along with contextual pseudo-depth maps, to learn which object boundary may be occluded and therefore, extended to hallucinate the complete extent of an object. This is followed by a content completion stage which is able to inpaint the occluded regions of an object. We benchmark our approach alongside a wide array of state-of-the-art methods on four datasets and show a dramatic improvement of upto 13% for amodal segmentation in an object's occluded region.

• The paper introduces a novel two-stage framework leveraging Stable Video Diffusion for high-quality video amodal segmentation and occluded region completion.
• The approach achieves state-of-the-art results, showing up to 13% improvement on SAIL-VOS and zero-shot generalization on real-world datasets like TAO-Amodal, particularly excelling in heavy occlusion.
• This advancement has practical implications for robotics and autonomous systems by enabling a better understanding of entire object shapes regardless of occlusion.

Analyzing "Using Diffusion Priors for Video Amodal Segmentation"

The paper "Using Diffusion Priors for Video Amodal Segmentation" introduces a novel methodology leveraging diffusion models for performing high-quality amodal segmentation in video sequences. This approach centers around the utilization of Stable Video Diffusion (SVD), a large-scale pretrained video diffusion model, which helps in achieving state-of-the-art performance on amodal segmentation tasks across both synthetic and real-world datasets.

Summary of Methodology

The authors detail a two-stage framework that first predicts amodal segmentation masks over a video sequence and then completes the RGB content in occluded regions. To achieve this, the model employs a conditional latent diffusion framework with a 3D U-Net backbone. The first stage outputs amodal masks by using given modal masks along with pseudo-depth information as input, leveraging strong shape priors and temporal consistencies that are inherently learned by diffusion models. The second stage takes these predicted masks and the modal content to inpaint the occluded areas, thus generating a coherent RGB completion.

Numerical Results and Performance

The paper presents strong empirical results, claiming improvements up to 13% in amodal segmentation metrics over existing methods on the SAIL-VOS dataset and highlighting its zero-shot generalization capabilities on real-world datasets like TAO-Amodal. The method notably excels in scenarios with heavy object occlusions and performs effectively across diverse object categories. It consistently outperforms image-based and other video-based segmentation baselines, showing increased performance in terms of mean Intersection over Union (mIoU), particularly on highly occluded frames.

Implications and Future Directions

Practically, these advancements in segmentation can significantly enhance applications in robotics and autonomous systems where understanding entire object shapes is crucial, regardless of occlusion. Theoretically, this work illustrates the potential of diffusion models in generating temporally consistent and spatially accurate predictions in complex tasks beyond traditional frame-based image processing.

Future developments could explore improvements in handling newly encountered object categories by further refining domain adaptation techniques. Additionally, the insights gained here could contribute to advancements in more generalized AI models capable of synthesizing content across varied domains.

Conclusion

The integration of diffusion priors into video amodal segmentation represents a significant contribution to the field, offering a robust, scalable solution to an inherently challenging problem. By capitalizing on advancements in video diffusion models and employing innovative training strategies, the proposed approach not only sets a new benchmark for segmentation tasks but also pushes the boundaries for future AI applications in dynamic visual environments.