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Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models (2312.13763v2)

Published 21 Dec 2023 in cs.CV and cs.LG

Abstract: Text-guided diffusion models have revolutionized image and video generation and have also been successfully used for optimization-based 3D object synthesis. Here, we instead focus on the underexplored text-to-4D setting and synthesize dynamic, animated 3D objects using score distillation methods with an additional temporal dimension. Compared to previous work, we pursue a novel compositional generation-based approach, and combine text-to-image, text-to-video, and 3D-aware multiview diffusion models to provide feedback during 4D object optimization, thereby simultaneously enforcing temporal consistency, high-quality visual appearance and realistic geometry. Our method, called Align Your Gaussians (AYG), leverages dynamic 3D Gaussian Splatting with deformation fields as 4D representation. Crucial to AYG is a novel method to regularize the distribution of the moving 3D Gaussians and thereby stabilize the optimization and induce motion. We also propose a motion amplification mechanism as well as a new autoregressive synthesis scheme to generate and combine multiple 4D sequences for longer generation. These techniques allow us to synthesize vivid dynamic scenes, outperform previous work qualitatively and quantitatively and achieve state-of-the-art text-to-4D performance. Due to the Gaussian 4D representation, different 4D animations can be seamlessly combined, as we demonstrate. AYG opens up promising avenues for animation, simulation and digital content creation as well as synthetic data generation.

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Authors (5)
  1. Huan Ling (23 papers)
  2. Seung Wook Kim (23 papers)
  3. Antonio Torralba (178 papers)
  4. Sanja Fidler (184 papers)
  5. Karsten Kreis (50 papers)
Citations (84)
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Summary

Align Your Gaussians: Advances in Text-to-4D Dynamic Scene Synthesis

The paper "Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models" presents an innovative approach to generating dynamic 4D content, significantly broadening the applicability of text-guided diffusion models. This research utilizes dynamic 3D Gaussian splatting as the cornerstone of its 4D representation, overlaying it with deformation fields to encapsulate temporal dynamics, thus effectively adding a fourth dimension to previously static 3D models. The resulting method, named Align Your Gaussians (AYG), capitalizes on the potential of compositional generation, blending feedback from text-to-image, text-to-video, and 3D-aware multiview diffusion models to synthesize realistic and temporally coherent 4D dynamic scenes.

Fundamentally, the paper illustrates how utilizing a combination of these models provides a more nuanced generative pipeline. The multiview image diffusion model, MVDream, serves as a prior for optimizing static 3D assets, which are crucial for initializing the 4D synthesis. The dynamic component of the framework leverages a newly trained text-to-video diffusion model with frame-rate conditioning, allowing AYG to achieve smooth, diverse, and contextually rich animations. Throughout the process, the system draws from the respective strengths of the employed models: temporal dynamics from the video model ensure motion coherence, while the image model guarantees the high-quality visual fidelity of each frame.

Key numerical results demonstrate that AYG outperforms existing methods in qualitative and quantitative benchmarks, as evidenced by user studies and detailed evaluations against MAV3D, a previously recognized approach in dynamic 4D synthesis. The AYG system, with its autoregressive capabilities, allows for the extension of animations beyond the traditional temporal limits of the baseline approaches, and its dynamic 3D Gaussian representation encourages intuitive composability in large-scale digital scenarios.

Several core innovations merit acknowledgment. First, the newly introduced motion amplification technique effectively boosts scene motion while maintaining stability within synthetic animations. This, combined with a modified JSD-based regularization scheme, ensures that the distribution of 3D Gaussians is optimally managed through the deformation process, fostering clearer and more vibrant animations. The approach prevents broad translations and scale changes, directing the model towards producing realistic motion dynamics. Furthermore, the autoregressive synthesis scheme introduced enables the extension of 4D sequences, a core advance allowing the synthesis of prolonged animations with changing text prompts, marking a first in the literature.

From a theoretical perspective, AYG broadens the scope of score distillation sampling by exploring the simultaneous use of multiple textual diffusion models. The researchers convincingly argue for the compositional utilization of these models, which they validate by achieving state-of-the-art results in dynamic 4D generation tasks. From a practical standpoint, the increased flexibility and quality in generated animations presented by AYG hold substantial implications for digital content creation, particularly in assembling complex virtual scenes and generating synthetic data for machine learning contexts.

Looking forward, AI researchers and developers could explore extending the work of this paper by addressing current limitations, such as developing methods to accommodate topological changes within the synthesized 3D shapes or extending the framework to object-centric synthesis. Moreover, future research might focus on integrating image-to-3D translation techniques to leverage personalized 3D representations within AYG's dynamic synthesis pipeline, possibly expanding the system's generative capabilities into areas of personalized virtual reality and augmented reality environments.

In conclusion, "Align Your Gaussians" advances the frontiers of text-driven 4D content generation, proposing innovations that significantly enhance the robustness and realism of animated digital scenes. Its creative yet analytically rigorous use of dynamic 3D Gaussian-based representations, along with meticulously composed diffusion models, represents a substantial stride in AI-driven generative modeling.

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