MoGAN: Improving Motion Quality in Video Diffusion via Few-Step Motion Adversarial Post-Training

Video diffusion models achieve strong frame-level fidelity butstill struggle with motion coherence, dynamics and realism, often pro-ducing jitter, ghosting, or implausible dynamics. A key limitation is thatthe standard denoising MSE objective provides no direct supervision ontemporal consistency, allowing models to achieve low loss while still gen-erating poor motion. We propose MoGAN, a motion-centric post-trainingframework that improves motion realism without reward models or hu-man preference data. Built atop a 3-step distilled video diffusion model,we train a DiT-based optical-flow discriminator to differentiate real fromgenerated motion, combined with a distribution-matching regularizer topreserve visual fidelity. With experiments on Wan2.1-T2V-1.3B, MoGANsubstantially improves motion quality across benchmarks. On VBench,MoGAN boosts motion score by +7.3% over the 50-step teacher and+13.3% over the 3-step DMD model. On VideoJAM-Bench, MoGAN im-proves motion score by +7.4% over the teacher and +8.8% over DMD,while maintaining comparable or even better aesthetic and image-qualityscores. A human study further confirms that MoGAN is preferred formotion quality (52% vs. 38% for the teacher; 56% vs. 29% for DMD).Overall, MoGAN delivers significantly more realistic motion without sac-rificing visual fidelity or efficiency, offering a practical path toward fast,high-quality video generation.