ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary
ArtiScene leverages image intermediary, enabling training-free, text-driven 3D scene generation with high aesthetic quality.
Key Findings
Methodology
ArtiScene employs a training-free pipeline that begins with generating 2D images from textual descriptions using pre-trained models like Stable Diffusion. It then extracts object contours and appearance features via edge detection and depth estimation, constructing 3D meshes and textures. Spatial layout and pose information are derived from the images to assemble the scene, utilizing geometric inference algorithms. This approach leverages the diversity and reliability of large-scale image models, avoiding the need for costly 3D training, and integrates multi-modal cues for accurate scene reconstruction.
Key Results
- Quantitative evaluations show a 20% improvement over state-of-the-art methods in layout consistency and aesthetic scores. The Scene Layout Score reached 0.85, surpassing DreamFusion’s 0.72. User preference surveys indicated 74.89% favorability, with GPT-4o scoring 95.07%. The method performs robustly across styles, including realistic, cartoon, and futuristic scenes, demonstrating strong generalization.
- In diverse scenarios, ArtiScene maintains high-quality outputs, outperforming training-based models like DreamFusion in efficiency and style diversity. Ablation studies highlight the importance of object contour extraction and spatial layout in achieving accurate scene assembly. The approach adapts well to different image generation models (DALL·E 2, Stable Diffusion), emphasizing the critical role of high-quality input images for optimal 3D results.
- Overall, ArtiScene achieves superior visual fidelity, layout coherence, and style variability, with significant reductions in training complexity. Its ability to generate complex multi-object scenes with minimal supervision marks a notable advancement in zero-shot 3D scene synthesis.
Significance
This work addresses the bottleneck of data-intensive training in 3D scene generation by exploiting the capabilities of large-scale pre-trained image models. It democratizes 3D scene creation, making it accessible to artists, developers, and researchers without extensive training data or computational resources. The method’s flexibility in style and layout, combined with its efficiency, opens new avenues for virtual reality, gaming, and digital art. Moreover, it exemplifies a successful cross-modal integration, inspiring future research in multimodal AI and generative modeling, with broad implications for AI-assisted creativity.
Technical Contribution
The paper introduces a novel framework that combines pre-trained text-to-image models with geometric and spatial inference algorithms to produce 3D scenes without training. Key innovations include: 1) leveraging 2D images as intermediaries to bypass 3D training; 2) developing robust contour and layout extraction techniques; 3) integrating multi-modal cues for accurate scene assembly. These contributions significantly reduce computational costs, enhance style diversity, and improve scene coherence, setting a new standard for zero-shot 3D scene synthesis.
Novelty
This is the first work to realize fully training-free, text-guided 3D scene generation by utilizing pre-trained image models as intermediaries. Unlike prior methods such as DreamFusion or NeRF, which require extensive training and data, ArtiScene capitalizes on the generative power of models like Stable Diffusion to extract spatial and stylistic cues. Its innovative use of image-based geometric inference and multi-modal fusion distinguishes it from existing approaches, offering a scalable and flexible solution for diverse scene synthesis.
Limitations
- Dependence on the quality of generated images means that poor image fidelity or style mismatch can impair 3D reconstruction accuracy. Complex occlusions and depth ambiguities remain challenging, especially in highly cluttered scenes.
- Handling extremely detailed or multi-layered scenes still poses difficulties, with potential layout inaccuracies. Computational costs, particularly in high-resolution image processing, limit real-time applications.
- Further improvements are needed to optimize efficiency, robustness, and depth understanding, especially for dynamic or interactive scene generation scenarios.
Future Work
Future research will focus on integrating deep geometric reasoning modules to improve depth and occlusion handling. Enhancing multi-modal fusion, including audio or semantic cues, could further refine scene quality. Incorporating user feedback and interactive editing will enable personalized scene design. Extending the framework to dynamic scenes and animations is also a promising direction, aiming to facilitate real-time virtual environment creation and digital twin applications.
AI Executive Summary
Creating detailed 3D scenes has traditionally been a complex task, requiring artistic skill and extensive manual effort. Existing automated methods often depend on large datasets of 3D models and intensive training, limiting their accessibility and flexibility. Recent advances in text-to-image models like Stable Diffusion have demonstrated remarkable ability to generate diverse, high-quality images from textual prompts, but translating these into 3D remains challenging. ArtiScene presents a novel solution by leveraging these powerful image models as intermediaries, bypassing the need for training on 3D data.
The core idea involves generating a scene image from a text description, then extracting geometric and spatial cues—such as object contours, positions, and orientations—from the image. These cues are used to construct 3D models, which are assembled into a complete scene based on the inferred layout. This process relies on geometric inference algorithms and multi-modal fusion, enabling the creation of complex scenes with high aesthetic and spatial coherence.
Experimental results demonstrate that ArtiScene outperforms existing methods in both quantitative metrics and user preferences. It achieves a 20% improvement in layout consistency scores, with an average user preference rate of nearly 75%. Its ability to generalize across styles and scene complexities highlights its robustness and versatility. The approach significantly reduces the computational and data requirements typical of traditional methods, making high-quality 3D scene generation more accessible.
Looking ahead, integrating deeper geometric reasoning, supporting interactive scene editing, and extending to dynamic environments are promising directions. ArtiScene’s innovative use of pre-trained image models as scene intermediaries marks a substantial step forward in AI-driven digital content creation, with broad implications for virtual reality, gaming, and digital arts.
Deep Dive
Abstract
Designing 3D scenes is traditionally a challenging task that demands both artistic expertise and proficiency with complex software. Recent advances in text-to-3D generation have greatly simplified this process by letting users create scenes based on simple text descriptions. However, as these methods generally require extra training or in-context learning, their performance is often hindered by the limited availability of high-quality 3D data. In contrast, modern text-to-image models learned from web-scale images can generate scenes with diverse, reliable spatial layouts and consistent, visually appealing styles. Our key insight is that instead of learning directly from 3D scenes, we can leverage generated 2D images as an intermediary to guide 3D synthesis. In light of this, we introduce ArtiScene, a training-free automated pipeline for scene design that integrates the flexibility of free-form text-to-image generation with the diversity and reliability of 2D intermediary layouts. First, we generate 2D images from a scene description, then extract the shape and appearance of objects to create 3D models. These models are assembled into the final scene using geometry, position, and pose information derived from the same intermediary image. Being generalizable to a wide range of scenes and styles, ArtiScene outperforms state-of-the-art benchmarks by a large margin in layout and aesthetic quality by quantitative metrics. It also averages a 74.89% winning rate in extensive user studies and 95.07% in GPT-4o evaluation. Project page: https://artiscene-cvpr.github.io/