Motion Appearance Neighboring Space

Motion appearance neighboring space research focuses on accurately modeling how an object's appearance changes due to its motion, particularly for human avatars and video object segmentation. Current efforts leverage transformer architectures and novel representations like animatable 3D Gaussians to capture this complex interplay, often employing techniques like masked prediction and bilateral attention to improve the accuracy of motion and appearance estimation from limited data. This work is significant for advancing realistic human rendering, improving video understanding tasks like object segmentation, and enhancing the efficiency of self-supervised video pre-training.