SoPo: Text-to-Motion Generation Using Semi-Online Preference Optimization
This work addresses a critical but largely unexplored problem in text-to-motion generation for the creative industry, offering an incremental improvement over existing preference alignment methods.
The paper tackles the problem of fine-tuning text-to-motion models to consistently generate high-quality, human-preferred motions by introducing Semi-online Preference Optimization (SoPo), which combines online and offline data to overcome limitations like overfitting and biased sampling, resulting in improved performance with MM-Dist scores of 3.25% on MLD and 2.91% on MDM models.
Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fine-tuning text-to-motion models to consistently favor high-quality, human-preferred motions, a critical yet largely unexplored problem. In this work, we theoretically investigate the DPO under both online and offline settings, and reveal their respective limitation: overfitting in offline DPO, and biased sampling in online DPO. Building on our theoretical insights, we introduce Semi-online Preference Optimization (SoPo), a DPO-based method for training text-to-motion models using "semi-online" data pair, consisting of unpreferred motion from online distribution and preferred motion in offline datasets. This method leverages both online and offline DPO, allowing each to compensate for the other's limitations. Extensive experiments demonstrate that SoPo outperforms other preference alignment methods, with an MM-Dist of 3.25% (vs e.g. 0.76% of MoDiPO) on the MLD model, 2.91% (vs e.g. 0.66% of MoDiPO) on MDM model, respectively. Additionally, the MLD model fine-tuned by our SoPo surpasses the SoTA model in terms of R-precision and MM Dist. Visualization results also show the efficacy of our SoPo in preference alignment. Project page: https://xiaofeng-tan.github.io/projects/SoPo/ .