2023-12-06 更新
GaussianHead: Impressive 3D Gaussian-based Head Avatars with Dynamic Hybrid Neural Field
Authors:Jie Wang, Xianyan Li, Jiucheng Xie, Feng Xu, Hao Gao
Previous head avatar methods have mostly relied on fixed explicit primitives (mesh, point) or implicit surfaces (Sign Distance Function) and volumetric neural radiance field, it challenging to strike a balance among high fidelity, training speed, and resource consumption. The recent popularity of hybrid field has brought novel representation, but is limited by relying on parameterization factors obtained through fixed mappings. We propose GaussianHead: an head avatar algorithm based on anisotropic 3D gaussian primitives. We leverage canonical gaussians to represent dynamic scenes. Using explicit “dynamic” tri-plane as an efficient container for parameterized head geometry, aligned well with factors in the underlying geometry and tri-plane, we obtain aligned canonical factors for the canonical gaussians. With a tiny MLP, factors are decoded into opacity and spherical harmonic coefficients of 3D gaussian primitives. Finally, we use efficient differentiable gaussian rasterizer for rendering. Our approach benefits significantly from our novel representation based on 3D gaussians, and the proper alignment transformation of underlying geometry structures and factors in tri-plane eliminates biases introduced by fixed mappings. Compared to state-of-the-art techniques, we achieve optimal visual results in tasks such as self-reconstruction, novel view synthesis, and cross-identity reenactment while maintaining high rendering efficiency (0.12s per frame). Even the pores around the nose are clearly visible in some cases. Code and additional video can be found on the project homepage.
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GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians
Authors:Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, Matthias Nießner
We introduce GaussianAvatars, a new method to create photorealistic head avatars that are fully controllable in terms of expression, pose, and viewpoint. The core idea is a dynamic 3D representation based on 3D Gaussian splats that are rigged to a parametric morphable face model. This combination facilitates photorealistic rendering while allowing for precise animation control via the underlying parametric model, e.g., through expression transfer from a driving sequence or by manually changing the morphable model parameters. We parameterize each splat by a local coordinate frame of a triangle and optimize for explicit displacement offset to obtain a more accurate geometric representation. During avatar reconstruction, we jointly optimize for the morphable model parameters and Gaussian splat parameters in an end-to-end fashion. We demonstrate the animation capabilities of our photorealistic avatar in several challenging scenarios. For instance, we show reenactments from a driving video, where our method outperforms existing works by a significant margin.
PDF Project page: https://shenhanqian.github.io/gaussian-avatars
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FlashAvatar: High-Fidelity Digital Avatar Rendering at 300FPS
Authors:Jun Xiang, Xuan Gao, Yudong Guo, Juyong Zhang
We propose FlashAvatar, a novel and lightweight 3D animatable avatar representation that could reconstruct a digital avatar from a short monocular video sequence in minutes and render high-fidelity photo-realistic images at 300FPS on a consumer-grade GPU. To achieve this, we maintain a uniform 3D Gaussian field embedded in the surface of a parametric face model and learn extra spatial offset to model non-surface regions and subtle facial details. While full use of geometric priors can capture high-frequency facial details and preserve exaggerated expressions, proper initialization can help reduce the number of Gaussians, thus enabling super-fast rendering speed. Extensive experimental results demonstrate that FlashAvatar outperforms existing works regarding visual quality and personalized details and is almost an order of magnitude faster in rendering speed. Project page: https://ustc3dv.github.io/FlashAvatar/
PDF Project page: https://ustc3dv.github.io/FlashAvatar/
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InvertAvatar: Incremental GAN Inversion for Generalized Head Avatars
Authors:Xiaochen Zhao, Jingxiang Sun, Lizhen Wang, Yebin Liu
While high fidelity and efficiency are central to the creation of digital head avatars, recent methods relying on 2D or 3D generative models often experience limitations such as shape distortion, expression inaccuracy, and identity flickering. Additionally, existing one-shot inversion techniques fail to fully leverage multiple input images for detailed feature extraction. We propose a novel framework, \textbf{Incremental 3D GAN Inversion}, that enhances avatar reconstruction performance using an algorithm designed to increase the fidelity from multiple frames, resulting in improved reconstruction quality proportional to frame count. Our method introduces a unique animatable 3D GAN prior with two crucial modifications for enhanced expression controllability alongside an innovative neural texture encoder that categorizes texture feature spaces based on UV parameterization. Differentiating from traditional techniques, our architecture emphasizes pixel-aligned image-to-image translation, mitigating the need to learn correspondences between observation and canonical spaces. Furthermore, we incorporate ConvGRU-based recurrent networks for temporal data aggregation from multiple frames, boosting geometry and texture detail reconstruction. The proposed paradigm demonstrates state-of-the-art performance on one-shot and few-shot avatar animation tasks.
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HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
Authors:Helisa Dhamo, Yinyu Nie, Arthur Moreau, Jifei Song, Richard Shaw, Yiren Zhou, Eduardo Pérez-Pellitero
3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fields. Real-time rendering is a highly desirable goal for real-world applications. We propose HeadGaS, the first model to use 3D Gaussian Splats (3DGS) for 3D head reconstruction and animation. In this paper we introduce a hybrid model that extends the explicit representation from 3DGS with a base of learnable latent features, which can be linearly blended with low-dimensional parameters from parametric head models to obtain expression-dependent final color and opacity values. We demonstrate that HeadGaS delivers state-of-the-art results in real-time inference frame rates, which surpasses baselines by up to ~2dB, while accelerating rendering speed by over x10.
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