HyperMotionX: The Dataset and Benchmark with DiT-Based Pose-Guided Human Image Animation of Complex Motions

ArXi:2505.22977v2 Announce Type: replace Recent advances in diffusion models have significantly improved conditional video generation, particularly in the pose-guided human image animation task. Although existing methods are capable of generating high-fidelity and time-consistent animation sequences in regular motions and static scenes. However there are still obvious limitations when facing complex human body motions that contain highly dynamic, non-standard motions, and the lack of a high-quality benchmark for evaluation of complex human motion animations.