RealDPO: Real or Not Real, that is the Preference
Authors: name: Guo Cheng, name: Danni Yang, name: Ziqi Huang, name: Jianlou Si, name: Chenyang Si, name: Ziwei Liu

Video generative models have recently achieved notable advancements in
synthesis quality. However, generating complex motions remains a critical
challenge, as existing models often struggle to produce natural, smooth, and
contextually consistent movements. This gap between generated and real-world
motions limits their practical applicability. To address this issue, we
introduce RealDPO, a novel alignment paradigm that leverages real-world data as
positive samples for preference learning, enabling more accurate motion
synthesis. Unlike traditional supervised fine-tuning (SFT), which offers
limited corrective feedback, RealDPO employs Direct Preference Optimization
(DPO) with a tailored loss function to enhance motion realism. By contrasting
real-world videos with erroneous model outputs, RealDPO enables iterative
self-correction, progressively refining motion quality. To support
post-training in complex motion synthesis, we propose RealAction-5K, a curated
dataset of high-quality videos capturing human daily activities with rich and
precise motion details. Extensive experiments demonstrate that RealDPO
significantly improves video quality, text alignment, and motion realism
compared to state-of-the-art models and existing preference optimization
techniques.