# Video processing

VISION XL is a framework that addresses high-definition video inverse problems using potential diffusion models. It optimizes video processing efficiency and time through a pseudo-batch consistency sampling strategy and batch consistency inversion methods, supporting multiple scales and high-resolution reconstructions. Key advantages of this technology include support for multi-scale and high-resolution reconstruction, memory and sampling time efficiency, and the use of the open-source potential diffusion model SDXL. By integrating SDXL, it achieves state-of-the-art video reconstruction across various spatio-temporal inverse problems, including complex frame averaging and combinations of spatial degradations such as deblurring, super-resolution, and restoration.

Meta Segment Anything Model 2 (SAM 2) is a next-generation model developed by Meta for real-time, promptable object segmentation in videos and images. It achieves state-of-the-art performance and supports zero-shot generalization, allowing it to be applied to previously unseen visual content without needing custom adaptation. The release of SAM 2 follows an open science approach, with code and model weights shared under the Apache 2.0 license, and the SA-V dataset shared under the CC BY 4.0 license.

LLaVA-NeXT is a large multimodal model that handles multi-image, video, 3D, and single-image data through a unified interleaved data format, demonstrating its joint training abilities across different visual data modalities. The model has achieved leading results in multi-image benchmarks and has increased the performance or maintained performance of previous stand-alone tasks through appropriate data mixing in various scenarios.

This product offers a novel framework for smoothing jump cuts, especially in dialogue videos. It leverages the appearance of the subject in the video, using DensePose keypoints and facial landmark-driven intermediate representations to fuse information from other source frames. To achieve motion, it interpolates keypoints and landmarks between surrounding end frames. Then, it uses an image transformation network to synthesize pixels from the keypoints and source frames. Since keypoints may contain errors, a cross-modal attention mechanism is proposed to select and choose the most suitable source for each keypoint. By utilizing this intermediate representation, our method can achieve stronger results compared to strong video interpolation baselines. We demonstrate our method on various jump cuts in dialogue videos, such as removing filler words, pauses, and even random cuts. Our experiments show that we can achieve seamless transitions, even in challenging cases like head rotations or intense movements in dialogue heads.