Hallo4D: Mitigating Hallucinations in 3D/4D Generation
Hongbo Wang · hf · 2026-07-16
The authors propose **Hallo4D**, a unified, model-agnostic framework designed to mitigate "spatial/temporal hallucinations" in 3D/4D generation. The paper notes that existing 3D generation methods often rely on 2D diffusion supervision but lack explicit geometric consistency mechanisms, leading to repetitive structures and misaligned geometry. In 4D generation, this further causes jittering, identity flickering, and structural drift. To address this, Hallo4D adopts a **generation-detection-correction** pipeline: it first uses a Large Multimodal Model (LMM) to identify and summarize inconsistencies across multi-view/multi-frame outputs, and then corrects them via image-space-based "consistency optimization". Method details also include: - A multi-model voting-based LMM selector to evaluate candidate correction plans - No need for retraining or architectural changes - Introduction of motion-aware keyframe sampling, LMM-guided initialization, and appearance alignment - Additional integration of exposure-aware optimization and visibility pruning Experimental results show that it outperforms strong baselines across various 3D/4D generation scenarios.
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