October 2, 2025Open Access

Vision-Language Models as Differentiable Semantic and Spatial Rewards for Text-to-3D Generation

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VLM3D shows improved semantic fidelity and geometric coherence compared to previous methods, enhancing overall 3D generation quality.
Experiments demonstrate VLM3D's ability to accurately render complex scenes, showcasing its effectiveness across diverse objects.
The integration of vision-language models offers strong spatial understanding, addressing limitations in prior SDS-based frameworks.
By leveraging rich language-grounded supervision, VLM3D achieves finer prompt alignments, leading to better contextual accuracy.

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VLM3D significantly improves semantic alignment and spatial correctness in 3D generation, suggesting promising advancements in object relationships.

Bai et al. (2025) studied this question.

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VLM3D significantly improves semantic alignment and spatial correctness in 3D generation, suggesting promising advancements in object relationships.