SVAD: From Single Image to 3D Avatar via Synthetic Data Generation with Video Diffusion and Data Augmentation (2505.05475v1)

Abstract: Creating high-quality animatable 3D human avatars from a single image remains a significant challenge in computer vision due to the inherent difficulty of reconstructing complete 3D information from a single viewpoint. Current approaches face a clear limitation: 3D Gaussian Splatting (3DGS) methods produce high-quality results but require multiple views or video sequences, while video diffusion models can generate animations from single images but struggle with consistency and identity preservation. We present SVAD, a novel approach that addresses these limitations by leveraging complementary strengths of existing techniques. Our method generates synthetic training data through video diffusion, enhances it with identity preservation and image restoration modules, and utilizes this refined data to train 3DGS avatars. Comprehensive evaluations demonstrate that SVAD outperforms state-of-the-art (SOTA) single-image methods in maintaining identity consistency and fine details across novel poses and viewpoints, while enabling real-time rendering capabilities. Through our data augmentation pipeline, we overcome the dependency on dense monocular or multi-view training data typically required by traditional 3DGS approaches. Extensive quantitative, qualitative comparisons show our method achieves superior performance across multiple metrics against baseline models. By effectively combining the generative power of diffusion models with both the high-quality results and rendering efficiency of 3DGS, our work establishes a new approach for high-fidelity avatar generation from a single image input.

SVAD: From Single Image to 3D Avatar via Synthetic Data Generation with Video Diffusion and Data Augmentation

The paper "SVAD: From Single Image to 3D Avatar via Synthetic Data Generation with Video Diffusion and Data Augmentation" presents a novel method named SVAD (Synthetic Video and Augmented Data) for generating high-fidelity 3D avatars from a single image. This approach addresses the challenges inherent in extracting complete 3D information from a single viewpoint through the innovative combination of video diffusion models and data augmentation techniques, thereby advancing the capabilities of 3D Gaussian Splatting (3DGS).

Technical Approach

The methodology merges the strengths of video diffusion models and 3DGS to generate realistic, animatable 3D avatars with consistent identity across various poses and viewpoints. The process begins with synthetic data generation using a video diffusion model. This model leverages pose-conditioned animations derived from a single input image, using pre-existing sequences such as the People Snapshot dataset for pose information. Frame generation undergoes two primary enhancements: identity preservation and image restoration modules. The identity preservation module ensures facial consistency via a comprehensive pipeline involving 3D head reconstruction from FLAME parameter tracking, while the image restoration module elevates the quality of high-frequency details using diffusion-based image processing techniques.

Training these avatars involves fitting SMPL-X parameters to synthetic data, followed by optimization of the 3DGS representation utilizing RGB reconstruction, SSIM, and LPIPS losses. This results in animatable avatars that can be rendered in real-time across novel views, overcoming the traditional dependency on monocular or multi-view datasets.

Experimental and Results

The authors conducted extensive evaluations demonstrating that SVAD outperforms existing state-of-the-art methods, notably in scenarios utilizing single-image inputs. The quantitative metrics cited include PSNR, SSIM, and LPIPS, all indicating superior performance in maintaining identity consistency and texture realism. A comparative analysis on datasets such as People Snapshot and THuman highlights the method's proficiency in generating detailed, expressive 3D avatars.

Implications and Future Work

SVAD opens pathways for applications in virtual reality, digital media production, and personalized avatars in online platforms by simplifying avatar creation from minimal data without sacrificing quality. The paper recognizes potential limitations, including issues relating to segmentation artifact inclusion, handling complex clothing deformations, and some inconsistencies in back-view generation due to the inherently ill-posed nature of single-image to 3D modeling. Future research is poised to focus on these limitations, exploring optimization techniques for computational efficiency and improving its adaptability to diverse clothing types and complex visual environments.

In conclusion, SVAD represents a robust advancement in 3D avatar generation techniques, particularly in its ability to synthesize animations from minimal information inputs. This approach facilitates broader accessibility to digital representation technologies, potentially transforming personal and professional digital interactions. As the field continues to grow, methods like SVAD are likely to influence how we approach visual content creation, human-computer interaction, and even digital identity management in virtual spaces.