MA-NeRF: Motion-Assisted Neural Radiance Fields for Face Synthesis from Sparse Images

• URL: http://arxiv.org/abs/2306.10350v2
• Date: Sat, 24 Jun 2023 13:14:35 GMT
• Title: MA-NeRF: Motion-Assisted Neural Radiance Fields for Face Synthesis from Sparse Images
• Authors: Weichen Zhang, Xiang Zhou, Yukang Cao, Wensen Feng, Chun Yuan
• Abstract summary: We propose a novel framework that can reconstruct a high-fidelity drivable face avatar and handle unseen expressions. At the core of our implementation are structured displacement feature and semantic-aware learning module. Our method achieves much better results than the current state-of-the-arts.
• Score: 21.811067296567252
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We address the problem of photorealistic 3D face avatar synthesis from sparse images. Existing Parametric models for face avatar reconstruction struggle to generate details that originate from inputs. Meanwhile, although current NeRF-based avatar methods provide promising results for novel view synthesis, they fail to generalize well for unseen expressions. We improve from NeRF and propose a novel framework that, by leveraging the parametric 3DMM models, can reconstruct a high-fidelity drivable face avatar and successfully handle the unseen expressions. At the core of our implementation are structured displacement feature and semantic-aware learning module. Our structured displacement feature will introduce the motion prior as an additional constraints and help perform better for unseen expressions, by constructing displacement volume. Besides, the semantic-aware learning incorporates multi-level prior, e.g., semantic embedding, learnable latent code, to lift the performance to a higher level. Thorough experiments have been doen both quantitatively and qualitatively to demonstrate the design of our framework, and our method achieves much better results than the current state-of-the-arts.

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