Motion Consistency

Motion consistency, in the context of computer vision and video generation, focuses on creating temporally coherent and realistic motion in generated videos or edited sequences. Current research heavily utilizes diffusion models, often incorporating techniques like masked modeling, flow matching, and distillation to improve efficiency and quality while maintaining consistency across frames. This work is crucial for advancing video generation, editing, and related applications like visual odometry and human motion capture, where accurate and fluid motion is paramount for realistic and useful outputs. The development of new metrics for evaluating motion consistency further strengthens the field's rigor and allows for more objective comparisons of different approaches.