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AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

Yixuan Li, Feng Tian, Shanyan Guan

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 29, 2026
    PubMed
    Summary
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    Rectified Score Distillation enhances text-to-3D generation by using view-conditioned scores to fix inconsistencies. AgonicDreamer produces detailed, multi-view consistent 3D content, overcoming the Janus problem.

    Area of Science:

    • Computer Vision
    • 3D Graphics
    • Artificial Intelligence

    Background:

    • Score Distillation Sampling (SDS) methods leverage pre-trained text-to-image diffusion models for text-to-3D generation.
    • Existing SDS approaches struggle with view-inconsistency, commonly known as the Janus problem, due to view-agnostic score estimation.

    Purpose of the Study:

    • To introduce a novel framework, AgonicDreamer, that generates multi-view consistent and photorealistic 3D content.
    • To address the limitations of view-agnostic score estimation in current text-to-3D generation techniques.

    Main Methods:

    • Propose Rectified Score Distillation (RSD) by formulating a view-conditioned reverse ordinary differential equation (ODE) based on camera poses.
    • Rectify standard, view-irrelevant scores to approximate gradients along the proposed view-conditioned ODE.

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  • Integrate rectified scores into a comprehensive framework for optimizing 3D representations.

    • Main Results:

    • AgonicDreamer successfully generates high-fidelity 3D assets with improved multi-view consistency.
    • The proposed Rectified Score Distillation effectively mitigates the Janus problem observed in previous methods.
    • Extensive experiments validate the framework's capability in producing photorealistic 3D content with fine-grained details.

    Conclusions:

    • Rectified Score Distillation offers a robust solution for achieving view-consistent text-to-3D generation.
    • AgonicDreamer represents a significant advancement in generating high-quality, consistent 3D content from textual descriptions.
    • The method provides a promising direction for future research in generative 3D modeling.