RealDPO: Real or Not Real, that is the Preference
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.
68f1e1d76e0bef323a68fffc
Guo Cheng
68f1e1d76e0bef323a68fffd
Danni Yang
68f1e1d76e0bef323a68fffe
Ziqi Huang
68f1e1d76e0bef323a68ffff
Jianlou Si
68f1e1d76e0bef323a690000
Chenyang Si
68f1e1d76e0bef323a690001
Ziwei Liu
RealDPO: Real or Not Real, that is the Preference
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.
RealDPO, a novel preference learning paradigm using real-world data, enhances motion realism in video generative models through Direct Preference Optimization and iterative self-correction.
RealDPO Direct Preference Optimization preference learning motion synthesis video generative models RealAction-5K text alignment motion realism