Under Review</p>\n","updatedAt":"2026-05-22T16:31:09.013Z","author":{"_id":"65560e8047adf6b4ebfc7062","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/a7uIL26DBYuHTiLOKSgQM.png","fullname":"Jin Lyu","name":"luoxue-star","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":1,"identifiedLanguage":{"language":"en","probability":0.3356461822986603},"editors":["luoxue-star"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/a7uIL26DBYuHTiLOKSgQM.png"],"reactions":[],"isReport":false}},{"id":"6a1105c5425f7f7a2e87ad18","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":358,"isUserFollowing":false},"createdAt":"2026-05-23T01:41:25.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Human Interaction-Aware 3D Reconstruction from a Single Image](https://huggingface.co/papers/2604.05436) (2026)\n* [WildDet3D: Scaling Promptable 3D Detection in the Wild](https://huggingface.co/papers/2604.08626) (2026)\n* [Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations](https://huggingface.co/papers/2604.27106) (2026)\n* [Contrastive Multi-Modal Hypergraph Reasoning for 3D Crowd Mesh Recovery](https://huggingface.co/papers/2605.13854) (2026)\n* [Anny-Fit: All-Age Human Mesh Recovery](https://huggingface.co/papers/2605.04728) (2026)\n* [OCH3R: Object-Centric Holistic 3D Reconstruction](https://huggingface.co/papers/2605.13018) (2026)\n* [Pose-Aware Diffusion for 3D Generation](https://huggingface.co/papers/2605.00345) (2026)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"<p>This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2604.05436\">Human Interaction-Aware 3D Reconstruction from a Single Image</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.08626\">WildDet3D: Scaling Promptable 3D Detection in the Wild</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.27106\">Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.13854\">Contrastive Multi-Modal Hypergraph Reasoning for 3D Crowd Mesh Recovery</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.04728\">Anny-Fit: All-Age Human Mesh Recovery</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.13018\">OCH3R: Object-Centric Holistic 3D Reconstruction</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.00345\">Pose-Aware Diffusion for 3D Generation</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code><span class=\"SVELTE_PARTIAL_HYDRATER contents\" data-target=\"UserMention\" data-props=\"{&quot;user&quot;:&quot;librarian-bot&quot;}\"><span class=\"inline-block\"><span class=\"contents\"><a href=\"/librarian-bot\">@<span class=\"underline\">librarian-bot</span></a></span> </span></span> recommend</code></p>\n","updatedAt":"2026-05-23T01:41:25.162Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":358,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6903219819068909},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.07604","authors":[{"_id":"6a108446d8ff13e4eeb25808","name":"Xuyi Hu","hidden":false},{"_id":"6a108446d8ff13e4eeb25809","name":"Jin Lyu","hidden":false},{"_id":"6a108446d8ff13e4eeb2580a","name":"Jiuming Liu","hidden":false},{"_id":"6a108446d8ff13e4eeb2580b","name":"Yebin Liu","hidden":false},{"_id":"6a108446d8ff13e4eeb2580c","name":"Silvia Zuffi","hidden":false},{"_id":"6a108446d8ff13e4eeb2580d","name":"Liang An","hidden":false},{"_id":"6a108446d8ff13e4eeb2580e","name":"Stefan Goetz","hidden":false}],"publishedAt":"2026-05-08T00:00:00.000Z","submittedOnDailyAt":"2026-05-22T00:00:00.000Z","title":"SAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild","submittedOnDailyBy":{"_id":"65560e8047adf6b4ebfc7062","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/a7uIL26DBYuHTiLOKSgQM.png","isPro":false,"fullname":"Jin Lyu","user":"luoxue-star","type":"user","name":"luoxue-star"},"summary":"3D animal reconstruction in the wild remains challenging due to large species variation, frequent occlusions, and the prevalence of multi-animal scenes, while existing methods predominantly focus on single-animal settings. We present SAM 3D Animal, the first promptable framework for multi-animal 3D reconstruction from a single image. Built on the SMAL+ parametric animal model, our method jointly reconstructs multiple instances and supports flexible prompts in the form of keypoints and masks which enable more reliable disambiguation in crowded and occluded scenes. To train such a model, we further introduce Herd3D, a multi-animal 3D dataset containing over 5K images, designed to increase diversity in species, interactions, and occlusion patterns. Experiments on the Animal3D, APTv2, and Animal Kingdom datasets show that our framework achieves state-of-the-art results over both existing model-based and model-free methods, demonstrating a scalable and effective solution for prompt-driven animal 3D reconstruction in the wild.","upvotes":0,"discussionId":"6a108446d8ff13e4eeb2580f","ai_summary":"SAM 3D Animal enables multi-animal 3D reconstruction from single images using a promptable framework based on SMAL+ model with improved disambiguation through keypoints and masks.","ai_keywords":["SMAL+","multi-animal 3D reconstruction","promptable framework","keypoints","masks","Herd3D dataset","model-based methods","model-free methods"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[],"acceptLanguages":["en"],"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.07604.md"}">

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