Implicit Preference Alignment Improves Hand Animation in Human Image Animation
A novel approach known as Implicit Preference Alignment (IPA) tackles the difficulties of producing realistic hand movements in human image animation. The intricacy of hand motions arises from their high degrees of freedom and complexity. Conventional reinforcement learning methods that rely on human feedback, such as direct preference optimization, necessitate strict preference pairs, which can be costly and impractical for dynamic hand movements. IPA circumvents the need for paired preference data by employing implicit reward maximization, enhancing the chances of generating high-quality samples while penalizing departures from the pretrained model. Additionally, the framework features a Hand-Aware Local Optimization mechanism. This research is available on arXiv with the paper ID 2605.07545.
Key facts
- IPA is a data-efficient post-training framework for human image animation.
- It addresses the challenge of generating high-fidelity hand motions.
- IPA eliminates the need for paired preference data.
- The framework is theoretically grounded in implicit reward maximization.
- It maximizes likelihood of self-generated high-quality samples.
- It penalizes deviations from the pretrained prior.
- Includes a Hand-Aware Local Optimization mechanism.
- Published on arXiv with ID 2605.07545.
Entities
Institutions
- arXiv