DreamCatalyst: Fast and High-Quality 3D Editing via Controlling Editability and Identity Preservation (2407.11394v3)

Abstract: Score distillation sampling (SDS) has emerged as an effective framework in text-driven 3D editing tasks, leveraging diffusion models for 3D-consistent editing. However, existing SDS-based 3D editing methods suffer from long training times and produce low-quality results. We identify that the root cause of this performance degradation is \textit{their conflict with the sampling dynamics of diffusion models}. Addressing this conflict allows us to treat SDS as a diffusion reverse process for 3D editing via sampling from data space. In contrast, existing methods naively distill the score function using diffusion models. From these insights, we propose DreamCatalyst, a novel framework that considers these sampling dynamics in the SDS framework. Specifically, we devise the optimization process of our DreamCatalyst to approximate the diffusion reverse process in editing tasks, thereby aligning with diffusion sampling dynamics. As a result, DreamCatalyst successfully reduces training time and improves editing quality. Our method offers two modes: (1) a fast mode that edits Neural Radiance Fields (NeRF) scenes approximately 23 times faster than current state-of-the-art NeRF editing methods, and (2) a high-quality mode that produces superior results about 8 times faster than these methods. Notably, our high-quality mode outperforms current state-of-the-art NeRF editing methods in terms of both speed and quality. DreamCatalyst also surpasses the state-of-the-art 3D Gaussian Splatting (3DGS) editing methods, establishing itself as an effective and model-agnostic 3D editing solution. See more extensive results on our project page: https://dream-catalyst.github.io.

• The paper presents a dual-mode SDS framework that accelerates 3D editing while balancing editability and identity preservation.
• It introduces decreasing timestep sampling and a novel Delta Denoising Score to optimize diffusion-based editing efficiently.
• Experiments show superior performance in prompt alignment, image similarity, and aesthetic quality compared to prior models.

Insights into DreamCatalyst: Fast and High-Quality 3D Editing via Controlling Editability and Identity Preservation

The paper presents "DreamCatalyst," a framework addressing the complexities associated with 3D scene editing through score distillation sampling (SDS). Existing SDS-based 3D editing methods face challenges such as extensive training times and edits that compromise either the editability or identity preservation of scenes. The authors reframe SDS-based editing as a diffusion reverse process, offering a more efficient approach that balances editability with identity preservation.

Objectives and Methodology

The primary aim of the paper is to enhance text-driven 3D editing by overcoming the limitations of prior models, notably Posterior Distillation Sampling (PDS), which struggles with slow editing processes and inferior quality due to its prioritization of identity preservation. To achieve its objectives, DreamCatalyst operates in two modes: a faster mode completing edits in approximately 25 minutes, and a high-quality mode taking under 70 minutes.

The paper introduces a novel objective function to recalibrate the balance between editability and identity preservation, factoring in the noise perturbations encountered during the diffusion process. This is realized through Delta Denoising Score (DDS), facilitating a diffusion-friendly optimization akin to SDEdit—a stochastic differential equation-based editing framework.

Key Contributions

• Generalized SDS-Based Framework: By integrating SDEdit within an SDS framework, DreamCatalyst brings a dual methodological perspective to 3D editing, thus ensuring theoretical and practical improvements in editing performances.
• Decreasing Timestep Sampling: To enhance training speed and quality, the paper introduces decreasing timestep sampling, capturing fine details by reducing information loss during high-noise phases while preserving identity in low-noise conditions.
• Use of FreeU Architecture: FreeU is implemented to suppress high-frequency features, amplifying low-frequency features that are key to maintaining identity during the editing process. FreeU allows enhancement of editability without additional computational cost, optimizing the performance compared to other techniques such as Low-Rank Adaptation (LoRA).

Numerical Results and Evaluation

Comprehensively assessed through both qualitative and quantitative metrics, DreamCatalyst achieves notable improvements over baseline methods like IN2N and PDS, particularly in terms of editability and identity preservation. Metrics such as CLIP directional similarity, CLIP image similarity, and aesthetic scoring confirm its superior performance. Furthermore, user studies indicate significant preference for DreamCatalyst's results when evaluated for prompt alignment, quality, and identity retention.

Implications and Future Directions

The implications of this research extend to both theoretical understanding and practical application. By successfully integrating FreeU and DDS under a diffusion dynamics framework, DreamCatalyst paves the way for future innovations in SDS-based 3D editing, fostering enhanced editability without identity loss. The introduction of decreasing timestep sampling signifies a forward leap in alleviating computational costs while maintaining high-quality outputs, broadening potential applications in automated 3D content creation.

Future research could expand on the design and optimization of model architectures to further mitigate trade-offs, and explore additional applications in various 3D editing domains. The paper sets a robust foundation for advancements that can exploit the underlying principles of diffusion processes in varied image and scene editing contexts, suggesting avenues for more nuanced control mechanisms in 3D content manipulation.