[2605.07545] Implicit Preference Alignment for Human Image Animation

Computer Science > Computer Vision and Pattern Recognition arXiv:2605.07545 (cs) [Submitted on 8 May 2026] Title:Implicit Preference Alignment for Human Image Animation Authors:Yuanzhi Wang, Xuhua Ren, Jiaxiang Cheng, Bing Ma, Kai Yu, Tianxiang Zheng, Qinglin Lu, Zhen Cui View a PDF of the paper titled Implicit Preference Alignment for Human Image Animation, by Yuanzhi Wang and 7 other authors View PDF HTML (experimental) Abstract:Human image animation has witnessed significant advancements, yet generating high-fidelity hand motions remains a persistent challenge due to their high degrees of freedom and motion complexity. While reinforcement learning from human feedback, particularly direct preference optimization, offers a potential solution, it necessitates the construction of strict preference pairs. However, curating such pairs for dynamic hand regions is prohibitively expensive and often impractical due to frame-wise inconsistencies. In this paper, we propose Implicit Preference Alignment (IPA), a data-efficient post-training framework that eliminates the need for paired preference data. Theoretically grounded in implicit reward maximization, IPA aligns the model by maximizing the likelihood of self-generated high-quality samples while penalizing deviations from the pretrained prior. Furthermore, we introduce a Hand-Aware Local Optimization mechanism to explicitly steer the alignment process toward hand regions. Experiments demonstrate that our method achieves effecti...