GaussianIP: Identity-Preserving Realistic 3D Human Generation via Human-Centric Diffusion Prior
This addresses the need for efficient and realistic 3D human generation for applications like virtual avatars and digital content creation, representing a strong incremental improvement over prior methods.
The paper tackles the problem of slow training and lack of fine details in text-guided 3D human generation by proposing GaussianIP, a two-stage framework that uses human-centric diffusion priors to generate identity-preserving 3D humans from text and image prompts, achieving higher visual quality with notably fewer training steps than existing methods.
Text-guided 3D human generation has advanced with the development of efficient 3D representations and 2D-lifting methods like Score Distillation Sampling (SDS). However, current methods suffer from prolonged training times and often produce results that lack fine facial and garment details. In this paper, we propose GaussianIP, an effective two-stage framework for generating identity-preserving realistic 3D humans from text and image prompts. Our core insight is to leverage human-centric knowledge to facilitate the generation process. In stage 1, we propose a novel Adaptive Human Distillation Sampling (AHDS) method to rapidly generate a 3D human that maintains high identity consistency with the image prompt and achieves a realistic appearance. Compared to traditional SDS methods, AHDS better aligns with the human-centric generation process, enhancing visual quality with notably fewer training steps. To further improve the visual quality of the face and clothes regions, we design a View-Consistent Refinement (VCR) strategy in stage 2. Specifically, it produces detail-enhanced results of the multi-view images from stage 1 iteratively, ensuring the 3D texture consistency across views via mutual attention and distance-guided attention fusion. Then a polished version of the 3D human can be achieved by directly perform reconstruction with the refined images. Extensive experiments demonstrate that GaussianIP outperforms existing methods in both visual quality and training efficiency, particularly in generating identity-preserving results. Our code is available at: https://github.com/silence-tang/GaussianIP.