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Overview of the GaussianVideo approach for neural video representation. Our method combines 3D Gaussian splatting with continuous camera motion modeling via Neural ODEs to handle dynamic scenes efficiently. The pipeline includes hierarchical learning strategies for both (a) spatial and (b) temporal domains, progressively refining Gaussians to capture fine details and smooth motion.
Efficient neural representations for dynamic video scenes are critical for applications ranging from video compression to interactive simulations. Yet, existing methods often face challenges related to high memory usage, lengthy training times, and temporal consistency. To address these issues, we introduce a novel neural video representation that combines 3D Gaussian splatting with continuous ...
To address these limitations, we propose a new video representation and compression method based on 2D Gaussian Splatting to eficiently handle video data. Our proposed deformable 2D Gaussian Splatting dynamically adapts the transformation of 2D Gaussians at each frame, significantly reducing memory cost.
2 Video Stabilization Using Kalman Filter We start by preprocessing frames to enhance and identify easily trackable features. Calculating optical flow reveals motion between frames, aiding in establishing an affine matrix that captures frame relationships. Utilizing the Kalman filter minimizes abrupt motions, ensuring smoother transitions.
The program runs smootly editing 4K footage but when I add Gaussian Blur and click on "track" it takes about 2 secs to track 1 video second. I'm tracking 1 face and it pretty small compared to the video itself.
To address these limitations, we propose a new video representation and compression method based on 2D Gaussian Splatting to efficiently handle video data. Our proposed deformable 2D Gaussian Splatting dynamically adapts the transformation of 2D Gaussians at each frame, significantly reducing memory cost.
Abstract Video representation is a long-standing problem that is crucial for various down- stream tasks, such as tracking, depth prediction, segmentation, view synthesis, and editing.
Yan and Shao [3] and Schuler et al. [4] constructed a neural network to estimate the blur kernel s by training network. However, most Gaussian-image deblurring approaches are focused on single image restoration. But in video deblurring, the reconstruction process can make use of additional data from neighboring frames.
The script opens a video file (eye_recording.flv) or captures video from a camera. It defines the region of interest (ROI) within the video frame to focus on the eye. The ROI is processed through grayscale conversion, Gaussian blur, and thresholding. Contours are detected and the largest one is highlighted with a bounding box and crosshairs.
Abstract 3D Gaussian splats have emerged as a revolutionary, effective, learned representation for static 3D scenes. This work explores 2D Gaussian splats as a new primitive for representing videos. We propose GaussianVideo, an approach to learning a set of 2D Gaussian splats that can effectively represent video frames. GaussianVideo incorporates the following techniques: (i) To exploit ...