DiffusionAvatars: Deferred Diffusion for High-fidelity 3D Head Avatars (2311.18635v2)

Abstract: DiffusionAvatars synthesizes a high-fidelity 3D head avatar of a person, offering intuitive control over both pose and expression. We propose a diffusion-based neural renderer that leverages generic 2D priors to produce compelling images of faces. For coarse guidance of the expression and head pose, we render a neural parametric head model (NPHM) from the target viewpoint, which acts as a proxy geometry of the person. Additionally, to enhance the modeling of intricate facial expressions, we condition DiffusionAvatars directly on the expression codes obtained from NPHM via cross-attention. Finally, to synthesize consistent surface details across different viewpoints and expressions, we rig learnable spatial features to the head's surface via TriPlane lookup in NPHM's canonical space. We train DiffusionAvatars on RGB videos and corresponding fitted NPHM meshes of a person and test the obtained avatars in both self-reenactment and animation scenarios. Our experiments demonstrate that DiffusionAvatars generates temporally consistent and visually appealing videos for novel poses and expressions of a person, outperforming existing approaches.

• The paper introduces a deferred diffusion framework that synthesizes high-fidelity 3D head avatars with controllable expressions and poses.
• It leverages neural parametric head models and TriPlane feature mapping to ensure spatial consistency and nuanced expression conditioning.
• Quantitative evaluations, including lower LPIPS scores and improved AKD and CSIM metrics, demonstrate its superior performance in avatar animation.

An Examination of Deferred Diffusion for 3D Head Avatar Synthesis

The paper "Diffusion: Deferred Diffusion for High-fidelity 3D Head Avatars" introduces a diffusion-based neural rendering framework aimed at synthesizing high-fidelity 3D head avatars with controllable facial expressions and head poses. This research is anchored in the broader context of computer vision and graphics, with particular application potential in augmented reality (AR), virtual reality (VR), teleconferencing, and digital entertainment sectors.

The proposed system, named DiffusionAvatar, integrates the synthesis capabilities of 2D diffusion models with the spatial consistency of 3D head representations. The foundation of this method is a Neural Parametric Head Model (NPHM) that accurately captures the geometry and expressions of a human head in a canonical space. NPHM meshes, obtained through fitting multi-view video data, serve as the geometric proxy to guide the image synthesis process. This approach aims to address the limitations traditional 2D methods face when striving for temporal consistency and the comprehensive control necessary for animatable 3D avatars.

Methodological Insights

The authors propose a Deferred Diffusion framework, which strings together multiple innovative components:

1. Rasterization and Feature Mapping: Using nvdiffrast for rasterization, the method produces geometry proxies by rendering NPHM meshes. Spatial features are mapped onto these proxies using a TriPlane lookup, enhancing expression details and view consistency due to their spatially adaptive nature.
2. Expression Conditioning: The system conditions the neural renderings on expression codes derived directly from the NPHM, using cross-attention within the latent diffusion model (LDM). This allows the synthesis of more nuanced facial expressions by improving signal interpretation of expression intents.
3. Diffusion-Based Neural Rendering: A pre-trained LDM serves the dual purpose of guiding sophisticated image synthesis and providing a stable prior, which aids in the generation of novel expressions and viewpoints.

Evaluation and Results

The paper demonstrates that DiffusionAvatar outperforms several state-of-the-art methods in both self-reenactment and avatar animation scenarios. Through qualitative and quantitative evaluations, the model exhibits superior visual quality (indicated by lower LPIPS scores) and expression reconstruction accuracy (evidenced by metrics such as AKD and CSIM).

The use of a user paper to assess avatar animation further bolsters the presented evidence, with users acknowledging enhancements in visual realism and expression fidelity. The method's emphasis on view consistency (measured with JOD) corroborates its effectiveness in creating temporally coherent and high-definition realistic avatars despite the inherent 2D limitations.

Implications and Future Directions

The paper's contributions present several implications and offer future research pathways. Practically, this model stands to benefit industries that require high-fidelity, interactive human representation, such as immersive media production and advanced telepresence. Theoretically, DiffusionAvatar showcases an effective amalgamation of 2D and 3D model capabilities and highlights the potential of cross-attention mechanisms for direct conditioning in diffusion models.

Future work may delve into more efficient iterations of diffusion models for real-time application scenarios, optimizing computational complexity without sacrificing the synthesis quality. The integration of lighting control into this framework could enhance its usability in more dynamic environments, aligning with the need for seamless augmentation in mixed-reality experiences.

In conclusion, the paper presents a technically adept framework that advances the capability of blending diffusion models with parametric head representations. This research fundamentally progresses the design of animatable avatars and demonstrates tangible improvements over existing methodologies, particularly in scenarios demanding high interactivity and precision in avatar-driven communication.