X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation (2312.00085v3)

Abstract: In recent times, automatic text-to-3D content creation has made significant progress, driven by the development of pretrained 2D diffusion models. Existing text-to-3D methods typically optimize the 3D representation to ensure that the rendered image aligns well with the given text, as evaluated by the pretrained 2D diffusion model. Nevertheless, a substantial domain gap exists between 2D images and 3D assets, primarily attributed to variations in camera-related attributes and the exclusive presence of foreground objects. Consequently, employing 2D diffusion models directly for optimizing 3D representations may lead to suboptimal outcomes. To address this issue, we present X-Dreamer, a novel approach for high-quality text-to-3D content creation that effectively bridges the gap between text-to-2D and text-to-3D synthesis. The key components of X-Dreamer are two innovative designs: Camera-Guided Low-Rank Adaptation (CG-LoRA) and Attention-Mask Alignment (AMA) Loss. CG-LoRA dynamically incorporates camera information into the pretrained diffusion models by employing camera-dependent generation for trainable parameters. This integration enhances the alignment between the generated 3D assets and the camera's perspective. AMA loss guides the attention map of the pretrained diffusion model using the binary mask of the 3D object, prioritizing the creation of the foreground object. This module ensures that the model focuses on generating accurate and detailed foreground objects. Extensive evaluations demonstrate the effectiveness of our proposed method compared to existing text-to-3D approaches. Our project webpage: https://xmu-xiaoma666.github.io/Projects/X-Dreamer/ .

• The paper introduces novel modules, CG-LoRA and AMA Loss, which integrate camera-specific parameters and refined attention to improve 3D asset generation.
• The methodology addresses the domain gap by embedding camera information and emphasizing foreground rendering for enhanced text-to-3D quality.
• Experiments show significant fidelity improvements over existing methods, promising advancements for virtual reality, gaming, and animation applications.

Overview of X-Dreamer: Bridging Text-to-2D and Text-to-3D Generation

The paper presents X-Dreamer, an innovative approach aimed at addressing the domain gap between text-to-2D and text-to-3D generation, thereby enhancing the quality of 3D content creation from textual descriptions. This domain gap, primarily due to dependencies on camera perspectives and the focus on foreground objects, is a notable challenge when leveraging pretrained 2D diffusion models for 3D asset generation. The authors introduce two core components in X-Dreamer: Camera-Guided Low-Rank Adaptation (CG-LoRA) and Attention-Mask Alignment (AMA) Loss, each designed to mitigate these challenges and ensure high-quality, text-driven 3D content creation.

Methodology

Camera-Guided Low-Rank Adaptation (CG-LoRA): This module aims to embed camera-related information directly into pretrained diffusion models by introducing camera-dependent trainable parameters. Unlike traditional methods that do not incorporate camera-specific parameters, CG-LoRA dynamically adapts the generation process according to camera attributes, enhancing model alignment with multiple perspectives intrinsic to 3D content.

Attention-Mask Alignment (AMA) Loss: This approach is proposed to refine the attention mechanism within pretrained diffusion models by utilizing binary masks that emphasize the 3D foreground object. AMA Loss ensures that the primary focus is on rendering accurate foreground elements, effectively bridging the innate 2D tendencies of generating both foreground and background seamlessly into the 3D synthesis domain.

Experimental Evaluation

The paper reports extensive experiments that highlight the significant improvements offered by X-Dreamer over established text-to-3D methodologies. The evaluation illustrates the model's capacity to maintain coherence with text prompts while ensuring the generated 3D objects are rendered with high realism and detail.

Numerical Results and Claims

The empirical results demonstrate substantial fidelity enhancement in 3D asset generation, especially compared to contemporaries like DreamFusion. While exact numerical metrics are not specified within the abstract, X-Dreamer’s proposed techniques show qualitative progress, reducing common issues such as the Janus Problem—a scenario where symmetry discrepancies arise in 3D reconstructions from 2D models.

Implications and Future Directions

The implications of this work extend across various domains. Practically, the enhanced fidelity of the generated 3D models makes X-Dreamer suitable for applications in virtual reality, gaming, and animation industries where high-quality 3D reconstruction from text is invaluable. Theoretically, the research offers a framework for future endeavors to explore the integration of camera-specific features in model training, potentially enhancing realism across AI-generated content. Future work might target the simultaneous generation of multiple distinct objects to overcome current limitations as identified by the authors.

In conclusion, X-Dreamer introduces a noteworthy approach to addressing the domain gap between 2D and 3D content generation, primarily through the innovations of CG-LoRA and AMA Loss. These contributions potentially set a precedent for subsequent methodologies aiming for high-fidelity, text-driven 3D asset creation.