BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis (2311.05521v2)

Abstract: Synthesizing photorealistic 4D human head avatars from videos is essential for VR/AR, telepresence, and video game applications. Although existing Neural Radiance Fields (NeRF)-based methods achieve high-fidelity results, the computational expense limits their use in real-time applications. To overcome this limitation, we introduce BakedAvatar, a novel representation for real-time neural head avatar synthesis, deployable in a standard polygon rasterization pipeline. Our approach extracts deformable multi-layer meshes from learned isosurfaces of the head and computes expression-, pose-, and view-dependent appearances that can be baked into static textures for efficient rasterization. We thus propose a three-stage pipeline for neural head avatar synthesis, which includes learning continuous deformation, manifold, and radiance fields, extracting layered meshes and textures, and fine-tuning texture details with differential rasterization. Experimental results demonstrate that our representation generates synthesis results of comparable quality to other state-of-the-art methods while significantly reducing the inference time required. We further showcase various head avatar synthesis results from monocular videos, including view synthesis, face reenactment, expression editing, and pose editing, all at interactive frame rates.

• The paper introduces a three-phase pipeline that extracts deformable polygon meshes and bakes appearance into static textures for efficient real-time rendering.
• The method leverages the FLAME model to drive realistic expression and pose animations, achieving interactive frame rates on commodity hardware.
• Experimental results show a significant reduction in inference time compared to NeRF approaches, enhancing applications in AR/VR, telepresence, and gaming.

BakedAvatar: A New Approach for Real-Time Head Avatar Synthesis

The paper "BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis" presents an innovative methodology aimed at addressing the computational limitations inherent in current neural radiance field (NeRF)-based high-fidelity human head avatar syntheses, by proposing a novel representation termed BakedAvatar. This method is explicitly designed for applicability within standard polygon rasterization pipelines, ensuring real-time rendering capability even on commodity hardware such as mobile phones and tablets—a significant advancement over current state-of-the-art methods.

Core Contributions

The central thesis of the paper rests on a three-phase pipeline: First, it seeks to extract deformable, multi-layer polygon meshes from learned isosurfaces. Second, it computes expression-, pose-, and view-dependent appearances that are subsequently integrated as static texture maps, thus facilitating a standard polygon rasterization approach. Third, through a fine-tuning procedure involving differential rasterization, the method optimizes the fidelity and accuracy of these synthetic avatars.

1. Mesh and Texture Extraction: The authors meticulously detail a process for learning continuous deformation, manifold, and radiance fields. These fields enable the extraction of mesh structures that are subsequently used to bake appearance into textures at significant computational advantage over volumetric ray-marching.
2. Expression and Pose Driven Animation: BakedAvatar exploits the FLAME model to enable realistic deformations, which allows for interactive frame rates in expression and pose editing applications.
3. Rendering Performance: Experimentation on real-time rendering showcases the method's efficacy across various applications, including view synthesis, face reenactment, and expression/pose editing. The numerical results indicate a significant reduction in inference time relative to comparable state-of-the-art systems, with the potential to run at interactive frame rates on a variety of devices, including laptops and mobile phones.

Implications and Future Directions

The implications of this work are multiplicative. From a practical perspective, its capacity to synthesize lifelike avatars efficiently could revolutionize fields like AR/VR, telepresence, and game development by reducing latency issues and computational overhead, thereby broadening the accessibility of high-fidelity avatar synthesis.

Theoretically, the proposed system opens new vistas in real-time rendering research, hinting at a broader applicability of "baking" strategies for neural-rendering across diverse media applications. It challenges the supremacy of fully implicit scene representations by leveraging efficient mesh-based approximations.

Avenues for future work might explore further optimizing the spatial and temporal resolution of extracted textures, enhancing the realism of dynamic lighting and shadows through advanced lighting models, and improving the adaptability of the method for full-body avatar synthesis.

Conclusion

"BakedAvatar" exemplifies a tangible step forward in real-time neural avatar rendering, marrying high-fidelity results with computational tractability in ways that current NeRF-based approaches have struggled to achieve. By transitioning from dense ray sampling techniques to efficient mesh-based polygonal rendering using layered neural fields, the authors not only affirm the viability of rasterization pipelines in producing realistic dynamic avatars but also broaden the horizon for real-time applications in various computational landscapes. This work stands as a compelling chapter in the ongoing evolution of avatar synthesis technologies.