Excited to introduce <strong>Hallo4D</strong>, the latest addition to our <strong>Hallo series</strong>!</p>\n<p>Hallo4D is a model-agnostic framework for reducing spatial and temporal hallucinations in 3D and 4D generation. Instead of retraining the underlying generator, it uses multimodal LLMs to detect inconsistencies across views and frames, summarize the observed issues, and guide consensus-based corrections.</p>\n<p>It targets common artifacts such as duplicated geometry, geometric misalignment, temporal jitter, identity flicker, and structural drift.</p>\n<p>With Hallo4D, we continue exploring how multimodal reasoning can serve as a general consistency critic for generative models. We would love to hear your thoughts and feedback!</p>\n","updatedAt":"2026-07-16T04:22:30.385Z","author":{"_id":"6a1547ddbdbb2f53b93ab213","avatarUrl":"/avatars/bf59ca5c69a9cdfecbb0f624bfe0562d.svg","fullname":"Hongbo Wang","name":"wafer-bob","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":5,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8944787383079529},"editors":["wafer-bob"],"editorAvatarUrls":["/avatars/bf59ca5c69a9cdfecbb0f624bfe0562d.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2607.12752","authors":[{"_id":"6a585bb169aa0f8878bddbd9","name":"Hongbo Wang","hidden":false},{"_id":"6a585bb169aa0f8878bddbda","name":"Huaibo Huang","hidden":false},{"_id":"6a585bb169aa0f8878bddbdb","name":"Jie Cao","hidden":false},{"_id":"6a585bb169aa0f8878bddbdc","name":"Jin Liu","hidden":false},{"_id":"6a585bb169aa0f8878bddbdd","name":"Haoyang Tong","hidden":false},{"_id":"6a585bb169aa0f8878bddbde","name":"Ran He","hidden":false}],"publishedAt":"2026-07-15T00:00:00.000Z","submittedOnDailyAt":"2026-07-16T00:00:00.000Z","title":"Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation","submittedOnDailyBy":{"_id":"6a1547ddbdbb2f53b93ab213","avatarUrl":"/avatars/bf59ca5c69a9cdfecbb0f624bfe0562d.svg","isPro":false,"fullname":"Hongbo Wang","user":"wafer-bob","type":"user","name":"wafer-bob"},"summary":"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. 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Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation
Abstract
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.
Community
Excited to introduce Hallo4D, the latest addition to our Hallo series!
Hallo4D is a model-agnostic framework for reducing spatial and temporal hallucinations in 3D and 4D generation. Instead of retraining the underlying generator, it uses multimodal LLMs to detect inconsistencies across views and frames, summarize the observed issues, and guide consensus-based corrections.
It targets common artifacts such as duplicated geometry, geometric misalignment, temporal jitter, identity flicker, and structural drift.
With Hallo4D, we continue exploring how multimodal reasoning can serve as a general consistency critic for generative models. We would love to hear your thoughts and feedback!
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Cite arxiv.org/abs/2607.12752 in a model README.md to link it from this page.
Cite arxiv.org/abs/2607.12752 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2607.12752 in a Space README.md to link it from this page.
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