A Scalable Synthetic Video Data Engine for World Exploration with Rich Annotations</p>\n","updatedAt":"2026-08-18T07:27:27.104Z","author":{"_id":"64a0d1efa2edde1105e0781a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64a0d1efa2edde1105e0781a/GEhCf33sHcjZZ_KpM64uu.jpeg","fullname":"YongtaoGe","name":"geyongtao","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":4,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6864259243011475},"editors":["geyongtao"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/64a0d1efa2edde1105e0781a/GEhCf33sHcjZZ_KpM64uu.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.15659","authors":[{"_id":"6a840933b153becad167701f","name":"Xiaojie Xu","hidden":false},{"_id":"6a840933b153becad1677020","name":"Zhengyuan Lin","hidden":false},{"_id":"6a840933b153becad1677021","name":"Runyi Li","hidden":false},{"_id":"6a840933b153becad1677022","name":"Yihao Liu","hidden":false},{"_id":"6a840933b153becad1677023","name":"Kaipeng Zhang","hidden":false},{"_id":"6a840933b153becad1677024","name":"Yongtao Ge","hidden":false}],"publishedAt":"2026-08-16T00:00:00.000Z","submittedOnDailyAt":"2026-08-18T00:00:00.000Z","title":"WorldRover: A Scalable Synthetic Video Data Engine for World Exploration with Rich Annotations","submittedOnDailyBy":{"_id":"64a0d1efa2edde1105e0781a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64a0d1efa2edde1105e0781a/GEhCf33sHcjZZ_KpM64uu.jpeg","isPro":false,"fullname":"YongtaoGe","user":"geyongtao","type":"user","name":"geyongtao"},"summary":"Learning to generate or reconstruct explorable worlds requires video paired with more than RGB: camera motion, scene geometry, temporal correspondence and, for interactive models, control signals. Real capture can provide some of these signals, but dense geometry and long-range correspondence usually rely on estimation or specialised instrumentation. Rendering provides these quantities directly, yet existing synthetic resources rarely combine them on the same frames while also supporting controlled changes of viewpoint and appearance. We introduce WorldRover, a data engine for generating richly annotated, long-range explorations of artist-built environments. At its core, WorldRover-Engine is an Unreal Engine pipeline that executes and offline-renders minute-scale routes while preserving their full trajectories and scene geometry. The same exploration can be replayed from first-person, third-person, and 360 panoramic cameras under different environmental states. Using WorldRover-Engine, we construct WorldRover-10M, whose sequences pair RGB with metric depth, camera trajectories, and trajectory-derived action signals throughout each exploration. Third-person subsets additionally provide dense optical flow, long-range 2D/3D point tracks with visibility, and a character trajectory distinct from the camera trajectory. The engine can render a traversal from first-person, third-person and 360 panoramic viewpoints, under different environmental states or with a neutral white material, while preserving the route and scene geometry. WorldRover therefore turns long-horizon world exploration into a scalable data-generation problem, providing supervision for models that must build, maintain, and revisit coherent representations of an explorable world.","upvotes":1,"discussionId":"6a840933b153becad1677025","projectPage":"https://alayalab.github.io/WorldRover/","ai_summary":"WorldRover is a synthetic data engine that generates long-range, richly annotated video sequences with depth, camera motion, and tracking signals to support training models for coherent world exploration.","ai_keywords":["metric depth","camera trajectories","action signals","optical flow","2D/3D point tracks","visibility","panoramic cameras"],"ai_summary_model":"thinkingmachines/Inkling-Small","organization":{"_id":"689f08c50df4fcf7fddc0b08","name":"AlayaLab","fullname":"Alaya Lab","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/63342778d92c5842ae728aef/dNCvNz9MMshksG2xspIbM.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":"689f08c50df4fcf7fddc0b08","name":"AlayaLab","fullname":"Alaya Lab","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/63342778d92c5842ae728aef/dNCvNz9MMshksG2xspIbM.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2608/2608.15659.md","query":{}}">
WorldRover: A Scalable Synthetic Video Data Engine for World Exploration with Rich Annotations
Abstract
WorldRover is a synthetic data engine that generates long-range, richly annotated video sequences with depth, camera motion, and tracking signals to support training models for coherent world exploration.
Learning to generate or reconstruct explorable worlds requires video paired with more than RGB: camera motion, scene geometry, temporal correspondence and, for interactive models, control signals. Real capture can provide some of these signals, but dense geometry and long-range correspondence usually rely on estimation or specialised instrumentation. Rendering provides these quantities directly, yet existing synthetic resources rarely combine them on the same frames while also supporting controlled changes of viewpoint and appearance. We introduce WorldRover, a data engine for generating richly annotated, long-range explorations of artist-built environments. At its core, WorldRover-Engine is an Unreal Engine pipeline that executes and offline-renders minute-scale routes while preserving their full trajectories and scene geometry. The same exploration can be replayed from first-person, third-person, and 360 panoramic cameras under different environmental states. Using WorldRover-Engine, we construct WorldRover-10M, whose sequences pair RGB with metric depth, camera trajectories, and trajectory-derived action signals throughout each exploration. Third-person subsets additionally provide dense optical flow, long-range 2D/3D point tracks with visibility, and a character trajectory distinct from the camera trajectory. The engine can render a traversal from first-person, third-person and 360 panoramic viewpoints, under different environmental states or with a neutral white material, while preserving the route and scene geometry. WorldRover therefore turns long-horizon world exploration into a scalable data-generation problem, providing supervision for models that must build, maintain, and revisit coherent representations of an explorable world.
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A Scalable Synthetic Video Data Engine for World Exploration with Rich Annotations
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Cite arxiv.org/abs/2608.15659 in a model README.md to link it from this page.
Cite arxiv.org/abs/2608.15659 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2608.15659 in a Space README.md to link it from this page.
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