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Recent progress in single-image 3D generation highlights the importance ofmulti-view coherency, leveraging 3D priors from large-scale diffusion modelspretrained on Internet-scale images. However, the aspect of novel-viewdiversity remains underexplored within the research landscape due to theambiguity in converting a 2D image into 3D content, where numerous potentialshapes can emerge. Here, we aim to address this research gap by simultaneouslyaddressing both consistency and diversity. Yet, striking a balance betweenthese two aspects poses a considerable challenge due to their inherenttrade-offs. This work introduces HarmonyView, a simple yet effective diffusionsampling technique adept at decomposing two intricate aspects in single-image3D generation: consistency and diversity. This approach paves the way for amore nuanced exploration of the two critical dimensions within the samplingprocess. Moreover, we propose a new evaluation metric based on CLIP image andtext encoders to comprehensively assess the diversity of the generated views,which closely aligns with human evaluators' judgments. In experiments,HarmonyView achieves a harmonious balance, demonstrating a win-win scenario inboth consistency and diversity.

HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D