OpenLongTail: Generative Scaling of Long-Tail Driving Data

Hugging Face Daily Papers Papers
autonomous-driving generative-ai long-tail-data multi-view-synthesis driving-policies open-source data-engine

Summary

OpenLongTail is an open-source generative data engine that transforms heterogeneous long-tail driving data into view-aligned multi-view assets for training robust autonomous driving policies, improving closed-loop driving robustness.

Scaling robust driving policies is fundamentally bottlenecked by the scarcity of edge cases in curated datasets. While the real world continuously captures these critical events, such long-tail events remain underutilized when collected from heterogeneous sources. Specifically, diverse but valuable in-the-wild long-tail videos lack the full view coverage required for training policy models, often missing multi-view poses or originating solely from monocular dash cameras. This modality gap prevents these ubiquitous observations from being converted into scalable training data for long-tail generalization. We introduce OpenLongTail, an open-source generative data engine for scaling autonomous driving policies under long-tail events. To transform heterogeneous data sources into view-aligned and temporally coherent multi-view assets that are useful for policy learning, we develop a pose-informed extrapolative view synthesis pipeline that generates the missing views. We further enhance cross-view consistency and the temporal alignment for the newly generated views by injecting Plücker ray geometry into the scalable generation engine. By synthesizing heterogeneous long-tail data, we observe a significant improvement in closed-loop driving robustness in handling long-tail events. By measuring the extrapolative view synthesis and pose metrics, we validate the effectiveness of OpenLongTail in visual fidelity, cross-view consistency, and ego-trajectory recovery.
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Source: https://huggingface.co/papers/2607.09655 Authors:

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Abstract

Scalingrobustdrivingpoliciesisfundamentallybottleneckedbythescarcityofedgecasesincurateddatasets.Whiletherealworldcontinuouslycapturesthesecriticalevents,suchlong-taileventsremainunderutilizedwhencollectedfromheterogeneoussources.Specifically,diversebutvaluablein-the-wildlong-tailvideoslackthefullviewcoveragerequiredfortrainingpolicymodels,oftenmissingmulti-viewposesororiginatingsolelyfrommonoculardashcameras.Thismodalitygappreventstheseubiquitousobservationsfrombeingconvertedintoscalabletrainingdataforlong-tailgeneralization.WeintroduceOpenLongTail,anopen-sourcegenerativedataengineforscalingautonomousdrivingpoliciesunderlong-tailevents.Totransformheterogeneousdatasourcesintoview-alignedandtemporallycoherentmulti-viewassetsthatareusefulforpolicylearning,wedevelopapose-informedextrapolativeviewsynthesispipelinethatgeneratesthemissingviews.Wefurtherenhancecross-viewconsistencyandthetemporalalignmentforthenewlygeneratedviewsbyinjectingPlückerraygeometryintothescalablegenerationengine.Bysynthesizingheterogeneouslong-taildata,weobserveasignificantimprovementinclosed-loopdrivingrobustnessinhandlinglong-tailevents.Bymeasuringtheextrapolativeviewsynthesisandposemetrics,wevalidatetheeffectivenessofOpenLongTailinvisualfidelity,cross-viewconsistency,andego-trajectoryrecovery.

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