CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models (2412.12093v1)

Abstract: Reconstructing photorealistic and dynamic portrait avatars from images is essential to many applications including advertising, visual effects, and virtual reality. Depending on the application, avatar reconstruction involves different capture setups and constraints $-$ for example, visual effects studios use camera arrays to capture hundreds of reference images, while content creators may seek to animate a single portrait image downloaded from the internet. As such, there is a large and heterogeneous ecosystem of methods for avatar reconstruction. Techniques based on multi-view stereo or neural rendering achieve the highest quality results, but require hundreds of reference images. Recent generative models produce convincing avatars from a single reference image, but visual fidelity yet lags behind multi-view techniques. Here, we present CAP4D: an approach that uses a morphable multi-view diffusion model to reconstruct photoreal 4D (dynamic 3D) portrait avatars from any number of reference images (i.e., one to 100) and animate and render them in real time. Our approach demonstrates state-of-the-art performance for single-, few-, and multi-image 4D portrait avatar reconstruction, and takes steps to bridge the gap in visual fidelity between single-image and multi-view reconstruction techniques.

• The paper proposes CAP4D, a two-stage method combining morphable multi-view diffusion and 4D avatar construction to create animatable portraits from variable input images.
• CAP4D achieves superior visual quality, identity consistency, 3D structure accuracy, and temporal coherence compared to existing methods.
• The method offers significant practical implications for content creation, reducing costs and enabling avatar generation from sparse data.

Overview of CAP4D: Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models

The paper "CAP4D: Creating Animatable 4D Portrait Avatars with Morphable Multi-View Diffusion Models" proposes an innovative method to generate photorealistic and dynamic 4D portrait avatars from a varied number of reference images. This approach is significant in its flexibility and applicability across different scenarios, such as in advertising, visual effects, and virtual reality.

Methodology

CAP4D employs a pipeline consisting of two main stages:

1. Morphable Multi-View Diffusion Model (MMDM): This step involves using diffusion models to predict novel views of a subject's portrait with unseen expressions, based on input reference images. It serves to bridge the gap in visual fidelity between single-image and multi-view reconstruction techniques.
2. Animatable 4D Avatar Construction: Using the views generated by the MMDM, a dynamic 4D avatar is constructed employing 3D Gaussian splatting. This enables real-time animation and rendering of the avatars.

The paper argues that the use of a morphable model for multi-view portraits significantly enhances the adaptability of the generated avatars, supporting various numbers of input images—from one to a hundred. It draws on the robust prior knowledge of human appearance encoded in the diffusion models and extends the capabilities of state-of-the-art techniques in the domain of portrait view synthesis.

Key Results

The researchers provide quantitative evaluations suggesting that CAP4D achieves superior results in areas such as visual quality, identity consistency, 3D structure accuracy, and temporal coherence, compared with existing methods. While specifics on metrics like PSNR, LPIPS, or jitter measurements aren't highlighted in the summary, the comprehensive improvement over baselines is emphasized.

Implications and Future Work

The potential implications for practical applications are vast, given that generating realistic human avatars with limited data can reduce costs and entry barriers in content creation, virtual communication, and entertainment. Moreover, it opens pathways for further refinement in synthesizing avatars from sparse data, which remains a critical challenge in AI-driven renderings.

Theoretically, the work suggests that morphable models conditioned through multi-view diffusion are promising for detail-rich and dynamic portrayal, potentially influencing new research directions in combining conditional diffusion models with 3D rendering techniques.

Looking to the future, developments could explore enhancing the computational efficiency of the model, especially around real-time application challenges, as well as expanding the surveillance of expressions beyond what's constrained by the current morphable model space. The expansion towards capturing full-body dynamics rather than just head avatars may also be a natural progression for extensive applications in immersive virtual experiences.

Conclusion

CAP4D marks a significant advancement in the field of avatar creation by integrating diffusion models and morphable models to produce animatable 4D avatars. Its capability to operate with a flexible number of input images without sacrificing visual fidelity offers substantial advancements over existing methods, making it a notable method for anyone in AI, computer graphics, or related fields interested in avatar synthesis.