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Text-to-image diffusion models pre-trained on billions of image-text pairshave recently enabled 3D content creation by optimizing a randomly initializeddifferentiable 3D representation with score distillation. However, theoptimization process suffers slow convergence and the resultant 3D models oftenexhibit two limitations: (a) quality concerns such as missing attributes anddistorted shape and texture; (b) extremely low diversity comparing totext-guided image synthesis. In this paper, we show that the conflict betweenthe 3D optimization process and uniform timestep sampling in score distillationis the main reason for these limitations. To resolve this conflict, we proposeto prioritize timestep sampling with monotonically non-increasing functions,which aligns the 3D optimization process with the sampling process of diffusionmodel. Extensive experiments show that our simple redesign significantlyimproves 3D content creation with faster convergence, better quality anddiversity.

DreamTime: An Improved Optimization Strategy for Diffusion-Guided 3D Generation