UT Austin and Meta researchers developed SteinDreamer: a breakthrough in text-to-3D asset synthesis using Stein scoring distillation for superior visual quality and accelerated convergence

Recent advances in text-to-image generation driven by diffusion models have sparked interest in text-guided 3D generation, with the goal of automating the creation of 3D assets for virtual reality, movies, and games. However, challenges arise in 3D synthesis due to the scarcity of high-quality data and the complexity of generative modeling with 3D representations. Score distillation techniques have emerged to address the lack of 3D data, using a 2D diffusion model. However, recognized problems include noisy gradients and instability arising from uncertainty in denoising and small batch sizes, resulting in slow convergence and suboptimal solutions.

Researchers at the University of Texas at Austin and Meta Reality Labs have developed SteinDreamer, which integrates the proposed Stein Score Distillation (SSD) into a 3D text generation process. SteinDreamer constantly addresses variation issues in the sheet music distillation process. In 3D object and scene generation, SteinDreamer outperforms DreamFusion and ProlificDreamer, delivering detailed textures and precise geometries and mitigating Janus and ghostly artifacts. SteinDreamer's reduced variation speeds up the convergence of 3D generation, resulting in fewer iterations.

Recent advances in text-to-image generation, driven by diffusion models, have sparked interest in text-guided 3D generation, with the goal of automating and accelerating the creation of 3D assets in virtual reality, movies, and games. . The study mentions score distillation, a predominant approach for synthesizing text-to-3D assets, and highlights the high variation of this method in gradient estimation. The study also mentions the fundamental works SDS of DreamFusion and VSD of ProlificDreamer, which are compared in the experiments with the proposed SteinDreamer. VSD is another variant of score distillation introduced by ProlificDreamer, which minimizes the KL divergence between the distribution of the image rendered from a 3D representation and the previous distribution.

The SSD technique incorporates control variables constructed by Stein's identity to reduce variation in score distillation for text-to-3D asset synthesis. The proposed SSD allows for the inclusion of flexible orientation priors and network architectures to explicitly optimize variance reduction. The general pipeline is implemented by instantiating the control variable with a monocular depth estimator. The effectiveness of SSD in reducing distillation variation and improving visual quality is demonstrated through experiments in generating 3D text at both the object and scene levels.

The proposed SteinDreamer, which incorporates the SSD technique, constantly improves the visual quality for object and scene generation in text-to-3D asset synthesis. SteinDreamer achieves faster convergence than existing methods due to more stable gradient updates. Qualitative results show that SteinDreamer generates views with fewer oversaturation and anti-aliasing artifacts than SDS. In challenging scene generation scenarios, SteinDreamer produces sharper results with better details than SDS and VSD. Experiments show that SSD effectively reduces distillation variation, improving visual quality in object and scene generation.

In conclusion, the study presents SteinDreamer, a more general solution to reduce variation in score distillation for text-to-3D asset synthesis. Based on the Stein identity, the proposed SSD technique effectively reduces the distillation variation and consistently improves the visual quality for both object and scene generation generations. SSD incorporates control variables built by Stein's identity, allowing for flexible guidelines and network architectures to optimize variation reduction. SteinDreamer achieves faster convergence than existing methods due to more stable gradient updates. Empirical evidence shows that VSD consistently outperforms SDS, indicating that the variance of their numerical estimate differs significantly. SSD, implemented in SteinDreamer, produces results with richer textures and lower level variation than SDS.

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