
DreamReward proposes a comprehensive framework to improve text-to-3D models by learning from human preference feedback, resulting in high-fidelity and aligned 3D results.


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DreamReward: Text-to-3D Generation with Human Preference


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GVGEN introduces a novel diffusion-based framework for efficient 3D Gaussian generation from text input, demonstrating superior performance in qualitative and quantitative assessments.


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GVGEN: Text-to-3D Generation with Volumetric Representation



DreamControl proposes a two-stage framework for text-to-3D generation, focusing on optimizing NeRF scenes as 3D self-prior and generating high-quality content with control-based score distillation.


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DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior



DreamControl proposes a two-stage framework to optimize 3D generation, focusing on geometry consistency and texture fidelity.
