Implicit Preference Alignment that removes the need for bad samples for preference optimization.</p>\n","updatedAt":"2026-05-13T08:06:48.669Z","author":{"_id":"6820a5143ccd1e0216105ff5","avatarUrl":"/avatars/4b65fadc31d72649b8c9c9958b992970.svg","fullname":"Yuanzhi Wang","name":"mdswyz","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8336986899375916},"editors":["mdswyz"],"editorAvatarUrls":["/avatars/4b65fadc31d72649b8c9c9958b992970.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.07545","authors":[{"_id":"6a02e425b823258e761237ee","user":{"_id":"6820a5143ccd1e0216105ff5","avatarUrl":"/avatars/4b65fadc31d72649b8c9c9958b992970.svg","isPro":false,"fullname":"Yuanzhi Wang","user":"mdswyz","type":"user","name":"mdswyz"},"name":"Yuanzhi Wang","status":"claimed_verified","statusLastChangedAt":"2026-05-13T07:53:35.218Z","hidden":false},{"_id":"6a02e425b823258e761237ef","name":"Xuhua Ren","hidden":false},{"_id":"6a02e425b823258e761237f0","name":"Jiaxiang Cheng","hidden":false},{"_id":"6a02e425b823258e761237f1","name":"Bing Ma","hidden":false},{"_id":"6a02e425b823258e761237f2","name":"Kai Yu","hidden":false},{"_id":"6a02e425b823258e761237f3","name":"Tianxiang Zheng","hidden":false},{"_id":"6a02e425b823258e761237f4","name":"Qinglin Lu","hidden":false},{"_id":"6a02e425b823258e761237f5","name":"Zhen Cui","hidden":false}],"publishedAt":"2026-05-08T00:00:00.000Z","submittedOnDailyAt":"2026-05-13T00:00:00.000Z","title":"Implicit Preference Alignment for Human Image Animation","submittedOnDailyBy":{"_id":"6820a5143ccd1e0216105ff5","avatarUrl":"/avatars/4b65fadc31d72649b8c9c9958b992970.svg","isPro":false,"fullname":"Yuanzhi Wang","user":"mdswyz","type":"user","name":"mdswyz"},"summary":"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 effective preference optimization to enhance hand generation quality, while significantly lowering the barrier for constructing preference data. Codes are released at https://github.com/mdswyz/IPA","upvotes":1,"discussionId":"6a02e425b823258e761237f6","projectPage":"https://github.com/mdswyz/IPA","githubRepo":"https://github.com/mdswyz/IPA","githubRepoAddedBy":"user","ai_summary":"Implicit Preference Alignment (IPA) addresses hand motion generation challenges through data-efficient post-training that eliminates need for paired preference data while using hand-aware local optimization for improved quality.","ai_keywords":["reinforcement learning from human feedback","direct preference optimization","implicit reward maximization","preference pairs","hand motion generation","post-training framework","hand-aware local optimization","implicit preference alignment"],"githubStars":2,"organization":{"_id":"6645f953c39288df638dbdd5","name":"Tencent-Hunyuan","fullname":"Tencent Hunyuan","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d22496c58f969c152bcefd/woKSjt2wXvBNKussyYPsa.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"620783f24e28382272337ba4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620783f24e28382272337ba4/zkUveQPNiDfYjgGhuFErj.jpeg","isPro":false,"fullname":"GuoLiangTang","user":"Tommy930","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"6645f953c39288df638dbdd5","name":"Tencent-Hunyuan","fullname":"Tencent Hunyuan","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/62d22496c58f969c152bcefd/woKSjt2wXvBNKussyYPsa.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.07545.md"}">

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