Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

Hugging Face Daily Papers Papers
3d-generation 4d-generation hallucination-mitigation multi-modal spatio-temporal lmm consistency

Summary

Hallo4D is a model-agnostic framework that leverages large multimodal language models to detect and correct spatial and temporal hallucinations in 3D and 4D generation, improving consistency across viewpoints and time without requiring retraining.

While recent advances in 3D generation have enabled impressive visual synthesis, existing methods often rely on 2D diffusion supervision without explicit mechanisms for geometric consistency, leading to spatial hallucinations such as duplicated structures and misaligned geometry. These issues become more severe in 4D generation, where maintaining consistency across viewpoints and temporal evolution introduces additional challenges, including jitter, identity flicker, and structural drift. We present Hallo4D, a unified and model-agnostic framework for mitigating spatiotemporal hallucinations in 3D and 4D content generation. Hallo4D introduces a generation-detection-correction paradigm that leverages large multimodal language models (LMMs) to identify and summarize spatial and temporal inconsistencies from multi-view and multi-frame renderings. These insights guide a consensus-driven image-space consistency optimization, where an LMM-based selector evaluates candidate corrections through multi-model voting, without requiring retraining or architectural modifications. To further improve temporal consistency and optimization efficiency, Hallo4D incorporates motion-aware keyframe sampling, LMM-guided initialization, and appearance alignment. We additionally introduce exposure-aware optimization and visibility pruning to enhance robustness under challenging viewpoints. Extensive experiments demonstrate that Hallo4D consistently outperforms strong baselines across diverse 3D and 4D generation settings, providing a scalable and generalizable solution for consistency-aware content generation.
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Paper page - Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

Source: https://huggingface.co/papers/2607.12752

Abstract

Whilerecentadvancesin3Dgenerationhaveenabledimpressivevisualsynthesis,existingmethodsoftenrelyon2Ddiffusionsupervisionwithoutexplicitmechanismsforgeometricconsistency,leadingtospatialhallucinationssuchasduplicatedstructuresandmisalignedgeometry.Theseissuesbecomemoreseverein4Dgeneration,wheremaintainingconsistencyacrossviewpointsandtemporalevolutionintroducesadditionalchallenges,includingjitter,identityflicker,andstructuraldrift.WepresentHallo4D,aunifiedandmodel-agnosticframeworkformitigatingspatiotemporalhallucinationsin3Dand4Dcontentgeneration.Hallo4Dintroducesageneration-detection-correctionparadigmthatleverageslargemultimodallanguagemodels(LMMs)toidentifyandsummarizespatialandtemporalinconsistenciesfrommulti-viewandmulti-framerenderings.Theseinsightsguideaconsensus-drivenimage-spaceconsistencyoptimization,whereanLMM-basedselectorevaluatescandidatecorrectionsthroughmulti-modelvoting,withoutrequiringretrainingorarchitecturalmodifications.Tofurtherimprovetemporalconsistencyandoptimizationefficiency,Hallo4Dincorporatesmotion-awarekeyframesampling,LMM-guidedinitialization,andappearancealignment.Weadditionallyintroduceexposure-awareoptimizationandvisibilitypruningtoenhancerobustnessunderchallengingviewpoints.ExtensiveexperimentsdemonstratethatHallo4Dconsistentlyoutperformsstrongbaselinesacrossdiverse3Dand4Dgenerationsettings,providingascalableandgeneralizablesolutionforconsistency-awarecontentgeneration.

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