The ability to understand videos at the level of pixels plays a key role in a wide range of computer vision applications. For example, a robot or autonomous vehicle relies on classifying each pixel in the video stream into semantic categories to holistically understand the surrounding environment, and video editing software needs to exploit the spatiotemporal context of video pixels to generate various visual effects. Despite the great progress of Deep Learning (DL) techniques, applying DL-based vision models to process video pixels remains practically challenging, due to the high volume of video data and the compute-intensive design of DL approaches. In this thesis, we aim to design efficient and robust deep models for pixel-level video understanding of high-level semantics, mid-level grouping, and low-level interpolation. Toward this goal, in Part I, we address the semantic analysis of video pixels with the task of Video Semantic Segmentation (VSS), which aims to assign pixel-level semantic labels to video frames. We introduce methods that utilize temporal redundancy and context to efficiently recognize video pixels without sacrificing performance. Extensive experiments on various datasets demonstrate our methods' effectiveness and efficiency on both common GPUs and edge devices. Then, in Part II, we show that pixel-level motion patterns help to differentiate video objects from their background. In particular, we propose a fast and efficient contour-based algorithm to group and separate motion patterns for video objects. Furthermore, we present learning-based models to solve the tracking of objects across frames. We show that by explicitly separating the object segmentation and object tracking problems, our framework achieves efficiency during both training and inference. Finally, in Part III, we study the temporal interpolation of pixels given their spatial-temporal context. We show that intermediate video frames can be inferred via interpolation in a very efficient way, by introducing the many-to-many splatting framework that can quickly warp and fuse pixels at any number of arbitrary intermediate time steps. We also propose a dynamic refinement mechanism to further improve the interpolation quality by reducing redundant computation. Evaluation on various types of datasets shows that our method can interpolate videos with state-of-the-art quality and efficiency. To summarize, we discuss and propose efficient pipelines for pixel-level video understanding tasks across high-level semantics, mid-level grouping, and low-level interpolation. The proposed models can contribute to tackling a wide range of real-world video perception and understanding problems in future research.