DreamWaltz-G: Expressive 3D Gaussian Avatars from Skeleton-Guided 2D Diffusion (2409.17145v1)

Abstract: Leveraging pretrained 2D diffusion models and score distillation sampling (SDS), recent methods have shown promising results for text-to-3D avatar generation. However, generating high-quality 3D avatars capable of expressive animation remains challenging. In this work, we present DreamWaltz-G, a novel learning framework for animatable 3D avatar generation from text. The core of this framework lies in Skeleton-guided Score Distillation and Hybrid 3D Gaussian Avatar representation. Specifically, the proposed skeleton-guided score distillation integrates skeleton controls from 3D human templates into 2D diffusion models, enhancing the consistency of SDS supervision in terms of view and human pose. This facilitates the generation of high-quality avatars, mitigating issues such as multiple faces, extra limbs, and blurring. The proposed hybrid 3D Gaussian avatar representation builds on the efficient 3D Gaussians, combining neural implicit fields and parameterized 3D meshes to enable real-time rendering, stable SDS optimization, and expressive animation. Extensive experiments demonstrate that DreamWaltz-G is highly effective in generating and animating 3D avatars, outperforming existing methods in both visual quality and animation expressiveness. Our framework further supports diverse applications, including human video reenactment and multi-subject scene composition.

• The paper introduces a novel framework using skeleton-guided score distillation to stabilize 2D diffusion for consistent 3D avatar generation.
• It presents a hybrid 3D Gaussian avatar representation that enables real-time rendering and expressive animations, capturing fine details like finger movements and facial expressions.
• Experimental results show superior geometric accuracy and avatar fidelity compared to traditional methods, promising impactful applications in VR and animated content.

Expressive 3D Gaussian Avatars from Skeleton-Guided 2D Diffusion

The paper "DreamWaltz-G: Expressive 3D Gaussian Avatars from Skeleton-Guided 2D Diffusion" presents a structured framework for the generation of animatable 3D avatars from textual descriptions. This research targets the optimization of text-to-3D avatar generation, addressing notable challenges such as maintaining 3D consistency and expressive animation capabilities.

Framework Overview

DreamWaltz-G introduces two key innovations:

1. Skeleton-Guided Score Distillation (SkelSD): This approach utilizes 3D human templates for extracting skeleton information to guide the 2D diffusion process. By integrating skeleton controls with 2D diffusion models, SkelSD enhances view and pose consistency, addressing typical issues like multiple faces and unnecessary appendages. This methodology substantially stabilizes the score distillation process, improving the visual quality of the avatars.
2. Hybrid 3D Gaussian Avatar Representation (H3GA): The proposed representation combines 3D Gaussian splatting with neural implicit fields and parameterized 3D meshes, allowing for real-time rendering and robust optimization. This hybrid model accommodates expressive animations, including finger movement and facial expressions, by leveraging both geometry and appearance features.

Experimental Results

The experiments demonstrate DreamWaltz-G's superiority over existing methods in generating visually consistent and animated 3D avatars. The paper reports extensive qualitative results showing improved geometric accuracy and appearance quality. The numerical results highlight the effectiveness of the hybrid representation and skeleton guidance in maintaining avatar fidelity during motion sequences.

Methodological Implications

DreamWaltz-G's framework offers practical benefits for applications requiring high-quality avatars, such as virtual reality, video games, and content creation in animated films. The integration of skeleton-guided controls points towards improved model stability and efficiency, reducing common artifacts seen in prior methodologies.

Theoretically, DreamWaltz-G expands the applicability of 2D diffusion models in 3D spaces by incorporating structural human priors, potentially influencing future work in avatar realism and expressivity. The framework's modularity also suggests further exploration in personalized avatar crafting based on input text refinement.

Future Directions

Anticipated developments could include enhancing the structural understanding in the diffusion process through more detailed conditioning, such as semantics and context from the textual prompt. Improvements in skeletal system modeling, potentially informed by advanced motion capture datasets, could refine the motion dynamics of generated avatars.

Moreover, extending the current approach to encompass interactive real-time modifications and adaptive mesh refinements might provide enriched user experiences in virtual platforms.

Conclusion

This research presents a compelling advancement in the domain of 3D avatar generation, grounded in effective diffusion strategies guided by skeletal structure. DreamWaltz-G not only achieves superior results in terms of visual and motion quality but also sets a foundation for future exploration into more holistic and integrated avatar creation systems. The paper serves as a significant contribution to the fields of computer graphics and AI-driven content generation.