Generalizable and Animatable Gaussian Head Avatar

Authors: xuangeng  chu
tatsuya  harada
riken  aip
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-15 08:15:16

Abstract: In this paper, we propose Generalizable and Animatable Gaussian head Avatar (GAGA) for one-shot animatable head avatar reconstruction.
Existing methods rely on neural radiance fields, leading to heavy rendering consumption and low reenactment speeds.
To address these limitations, we generate the parameters of 3D Gaussians from a single image in a single forward pass.
The key innovation of our work is the proposed dual-lifting method, which produces high-fidelity 3D Gaussians that capture identity and facial details.
Additionally, we leverage global image features and the 3D morphable model to construct 3D Gaussians for controlling expressions.
After training, our model can reconstruct unseen identities without specific optimizations and perform reenactment rendering at real-time speeds.
Experiments show that our method exhibits superior performance compared to previous methods in terms of reconstruction quality and expression accuracy.
We believe our method can establish new benchmarks for future research and advance applications of digital avatars.

Main paper: 
 introduction: 
 One-shot head avatar reconstruction has garnered significant attention in computer vision and graphics recently due to its great potential in applications such as virtual reality and online meetings. The typical problem involves faithfully recreating the source head from one image while precisely controlling expressions and poses. In recent years, many exploratory methods have achieved this goal using 2D generative models and 3D synthesizers. Some early 2D-based methods [Yin et al., 2022 , Ren et al., 2021] typically combine estimated deformation fields with generative networks to drive images. However, due to the lack of necessary 3D constraints and modeling, these methods struggle to maintain multi-view consistency of expressions and identities when head poses change significantly. Recently, Neural Radiance Fields (NeRF) [Mildenhall et al., 2020] have shown impressive results in head avatar synthesis, providing solutions using 3D synthesizers to achieve realistic details such as accessories and hair. However, some NeRF-based methods [Ma et al., 2023] require identity-specific training and optimization, and some methods [Li et al., 2023a , Chu et al., 2024 , Deng et al., 2024a] can't render in real-time during inference, limiting their application in certain scenarios. With the emergence of 3D Gaussian splatting [Kerbl et al., 2023] , some methods [Xu et al., 2024] have achieved real-time rendering. However, these methods still require specific training for each identity and fail to generalize to unseen identities, leaving the modeling of generalizable 3D Gaussian-based head models unexplored.To address these limitations, we introduce a novel 3D Gaussian-based framework for one-shot head avatar reconstruction. Given a single image, our framework reconstructs an animatable 3D Gaussian-based head avatar, achieving real-time expression control and rendering. Some examples are shown in Fig. 1 . The core challenge lies in faithfully reconstructing 3D Gaussians from a single image, as a 3D Gaussian typically requires multi-view input and millions of Gaussian points for detailed reconstruction. To address this, we propose a novel dual-lifting method that reconstructs the 3D Gaussians from one image. Specifically, instead of directly estimating Gaussian points from the image, we predict the lifting distances of each pixel relative to the image plane, and then map the image plane and lifted points back to 3D space based on the camera position. By predicting forward and backward lifting distances, we can form an almost closed Gaussian points distribution and reconstruct the head as completely as possible. This approach leverages the fine-grained features of the input image and significantly reduces the difficulty of predicting 3D Gaussian positions. We also utilize priors from 3D Morphable Models (3DMM) [Li et al., 2017] to further constrain the lifting distance, helping the model obtain correct 3D lifting and capture details from the source image. We then bind learnable features to the 3DMM vertices and construct expression Gaussians using image global features, 3DMM learnable features, and 3DMM point positions to ensure expression control capability. Finally, we use a neural renderer to refine the splatting-rendered results, producing the final reenacted image. Our model is learned from a large number of monocular portrait images and can be generalized to unseen identities after training. Experiments verify that our method performs better than previous methods in terms of reconstruction quality and expression accuracy, and achieves real-time reenactment and rendering speed.Our major contributions can be summarized as follows:• We propose GAGAvatar, which to our knowledge is the first generalizable 3D Gaussian head avatar framework that achieves single forward reconstruction and real-time reenactment.• To achieve this, we propose a dual-lifting method to lift Gaussians from a single image and introduce a method that uses 3DMM priors to constrain the lifting process.• We combine 3DMM priors and 3D Gaussians to accurately transfer expression information while avoiding redundant computations.2 Related WorkQuestion: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions scope?Answer: [Yes] Justification: The abstract and introduction include the claims made in the paper.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 
2d-based talking head synthesis: 
 The impressive performance of CNN and Generative Adversarial Networks (GAN) [Goodfellow et al., 2014 , Isola et al., 2017 , Karras et al., 2020] has inspired many methods for direct head image synthesis using 2D networks. A popular strategy of early works is inserting the expression and head pose features of the driving image into the 2D generative network to achieve realistic and animatable image generation. For example, these methods [Zakharov et al., 2019 , Burkov et al., 2020 , Zhou et al., 2021 , Wang et al., 2023] inject latent representations of expression into the U-Net backbone or StyleGAN-like [Karras et al., 2019] generators to transfer driving expressions to reenacted images. A recent trend in 2D-based talking head synthesis methods [Siarohin et al., 2019 , Ren et al., 2021 , Drobyshev et al., 2022 , Hong et al., 2022a , Zhang et al., 2023a] is to represent expressions and head poses as warp fields, performing expression transfer by deforming the source image to match the driving image. However, due to the lack of explicit understanding of the 3D geometry of head portraits, these methods often produce unrealistic distortions and undesired identity changes when there are significant pose and expression variations. Although some methods [Drobyshev et al., 2022 , Wang et al., 2021a , Ren et al., 2021 , Yin et al., 2022 , Zhang et al., 2023b] introduce 3D Morphable Models (3DMM) [Blanz and Vetter, 1999 , Paysan et al., 2009 , Li et al., 2017 , Gerig et al., 2018] into the 2D framework, they still lack the ability to control the viewpoint and achieve free-viewpoint rendering. Additionally, there are some audio-driven 2D control methods [Guo et al., 2021 , Tang et al., 2022 , Zhang et al., 2023b] , while flexible to use, cannot explicitly control facial expressions and poses, sometimes resulting in unsatisfactory outcomes. In contrast, our method uses an explicit 3D representation to enable free view control and realistic synthesis even under large pose variations.To achieve better 3D consistency in head avatars, many works have explored using 3D representations for reconstruction. Early methods [Xu et al., 2020 , Khakhulin et al., 2022] used 3DMM-based meshes [Li et al., 2017 , Gerig et al., 2018] to reconstruct head avatars. Since neural radiance fields (NeRF) [Mildenhall et al., 2020] have demonstrated excellent results, many recent methods [Li et al., 2023b ,a, Ma et al., 2023 , Yu et al., 2023 , Chu et al., 2024 , Ye et al., 2024 , Deng et al., 2024b ,a, Park et al., 2021a , Zheng et al., 2023 , Bai et al., 2023a , Ki et al., 2024 ] have adopted NeRF for head reconstruction. However, some approaches [Gafni et al., 2021 , Park et al., 2021a , Tretschk et al., 2021 , Hong et al., 2022b , Athar et al., 2022 , Park et al., 2021b , Gao et al., 2022 , Guo et al., 2021 , Bai et al., 2023b , Kirschstein et al., 2023 , Zheng et al., 2023 , Bai et al., 2023a , Zhao et al., 2023 , Zhang et al., 2024] require multi-view or single-view videos of specific identities for training, limiting generalization and raising privacy concerns due to the need for thousands of frames of personal image data. Additionally, some methods [Xu et al., 2023a , Tang et al., 2023 , Sun et al., 2022 , Xu et al., 2023b , Zhuang et al., 2022a , Sun et al., 2023] train generators to produce controllable head avatars from random noise, followed by inversion [Roich et al., 2022 , Xie et al., 2023] for identity-specific reconstruction. These methods often suffer from inversion accuracy limitations, failing to preserve the identity of the source image. There are also methods [Hong et al., 2022b , Zhuang et al., 2022b , Ma et al., 2023] to perform test-time optimization on the source image to obtain reconstructions, but the need for test-time optimization limits their applicability. To address these challenges, some works [Yu et al., 2023 , Li et al., 2023a ,b, Ma et al., 2024a , Yang et al., 2024 , Chu et al., 2024 , Ye et al., 2024 , Ma et al., 2024a , Deng et al., 2024b,a] focus on one-shot head reconstruction without test-time optimization. For example, GOHA [Li et al., 2023a] learns three tri-plane features to capture details. HideNeRF [Li et al., 2023b] utilizes multi-resolution tri-planes and a deformation field to generate reenactment images. GPAvatar [Chu et al., 2024 ] uses a point-based expression field and a multi tri-plane attention module to reconstruct head avatars. Real3DPortrait [Ye et al., 2024] generates a tri-plane from images and adds motion adapters to get reenactment images. CVTHead [Ma et al., 2024a] reconstructs head avatars using point-based neural rendering and a vertex-feature transformer.Portrait4D [Deng et al., 2024b] learns dynamic expression tri-plane from multi-view synthetic data, while Portrait4D-v2 [Deng et al., 2024a] learns from pseudo multi-view videos, addressing the lack of real video training in Portrait4D. However, these NeRF-based methods often face rendering speed limitations, preventing real-time application. Methods [Xu et al., 2024 , Li et al., 2024 , Hu et al., 2023 , Wang et al., 2024a , Ma et al., 2024b , Wang et al., 2024b] utilizing 3D Gaussian splatting [Kerbl et al., 2023] achieve excellent performance and rendering speed but require video data for identity-specific training, lacking generalization capabilities. In this paper, we propose a one-shot 3D Gaussian head avatar reconstruction method based on the dual-lifting method. Our method can generalize to unseen identities, achieves real-time rendering, and surpasses previous works in image quality.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes] Justification: We describe in the limitations and future work section.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
method: 
 An overview of the reenactment process of our method is shown in Fig. 2 . Given a source image I s , we first use DINOv2 [Darcet et al., 2023 , Oquab et al., 2023] to extract global and local features.Using the local features, we apply our proposed dual-lifting methods to predict the parameters and positions of two 3D Gaussians. Simultaneously, we assign learnable parameters to each vertex of the 3DMM [Li et al., 2017] model and predict another expression Gaussians using the combination of the global feature and vertex features. We directly use the vertex positions of the 3DMM model as the positions for expression Gaussians. Finally, we combine these 3D Gaussians and perform splatting to produce a coarse result image I c with the expression and pose of driving image I d , which is then further refined through a neural renderer to obtain the fine result image I f .In the following subsections, we describe the reconstruction branch based on dual-lifting in Sec. Figure 2 : Our method consists of two branches: a reconstruction branch (Sec. 3.1) and an expression branch (Sec. 3.2). We render dual-lifting and expressed Gaussians to get coarse results, and then use a neural renderer to get fine results. Only a small driving part needs to be run repeatedly to drive the expression, while the rest is executed only once.Given an input source image, our goal is to reconstruct a detailed 3D head avatar. To ensure stable modeling and learning, we impose certain constraints on the reconstruction process. First, we assume that the reconstructed head is always located at the origin in normalized 3D space. Second, the rotation of the head is modeled through changes in camera pose to ensure that the head itself is relatively stationary. We follow the same strategy when tracking 3DMM parameters and camera parameters from training and testing data. These constraints allow the model to effectively utilize the stable priors of the human head topology.Leveraging the success of 3D Gaussians splatting [Kerbl et al., 2023] in synthesis quality and rendering speed, we propose a dual-lifting method to reconstruct 3D Gaussians from a single image. Reconstructing 3D Gaussians typically requires millions of points, but obtaining such a dense density of Gaussian points from a single image is a challenging task, especially without test-time optimization.To address this problem, we propose a novel reconstruction method: the dual-lifting method. Briefly, we first get the local feature plane F local by a frozen DINOv2 backbone, and then predict the offsets of each pixel relative to the feature plane and the other necessary parameters (including color, opacity, scale and rotation), instead of predicting the 3D Gaussians directly. We then map the plane back to 3D space based on the camera pose and place the plane through the origin, which provides the 3D position and normal vector of the plane pixels. Finally, we can calculate the position of these 3D Gaussians in 3D space based on the predicted offsets, positions and normal vector. This process can be described as follows:EQUATIONG c,o,s,r = [E Conv0 (F local ), E Conv1 (F local )],(2) where p i is the initial points plane mapped based on the estimated camera pose of I s and passes through the origin. The size of p i is 296 × 296, which is consistent with the local feature F local . E Conv0,1 are convolutional networks, n s is the normal vector of p s , G pos is the position of reconstructed 3D Gaussians, and G c,o,s,r represents the color, opacity, scale, and rotation of 3D Gaussians.It's worth noting that while predicting one set of lifting distances from the plane is possible, we adopted a strategy of predicting forward and backward lifting separately. Our dual-lifting method aims to predict a complete 3D structure from a single source image, to achieve multi-view consistency during inference. If we predict only one set of lifting distances from the image plane, we may face some ambiguous situations during learning. For example, when we want to reconstruct a side view source image, predicting one set of lifting will simultaneously lift the point forward to the visible surface and backward to include the other side of the head. During this process, each pixel can be lifted to the visible surface or to the opposite surface, as both are justified, resulting in model performance degradation. Unlike single-lifting prediction, our dual-lifting strategy predicts forward and backward lifting separately, which eliminates ambiguities and stabilizes the optimization process.Our dual-lifting method effectively exploits the detailed information of the source image to reconstruct 3D Gaussians. At the same time, the two sets of predicted lifting points can form an almost closed Gaussian points distribution, thus enhancing the performance of large viewpoint changes. The 3D Gaussian generated by dual-lifting can be rendered from any viewpoint, producing static results. In the next section, we describe how to control the facial expressions of the generated avatar.Expression transfer is not a straightforward task, but the 3DMM [Li et al., 2017] provides us with a powerful tool to represent common facial expressions and decouple expressions from identity, thereby facilitating expression control. Our expression branch establishes 3D Gaussians based on the 3DMM vertices to control the expressions of the generated images. To achieve this, we bind learnable weights to each vertex in the 3DMM. Due to the stable semantics of 3DMM vertices, the features of these points correspond to facial positions such as the eyes and mouth.As shown in Fig. 2 , given the source image I s and driving image I d , we concatenate the global features F id with the learnable features of vertices. We then use a MLP to predict the Gaussian parameters (excluding position) of each point from these features, and use the position of the 3DMM vertices. Here we combine the global features F id of the source image when predicting the expression Gaussians. This will introduce identity information to the expression branch and enhance the identity consistency under various expressions, as confirmed by our experiments. Throughout the driving process, we only need to infer the Gaussians of the reconstruction branch and expression branch once. Reenactment is achieved by modifying the camera pose and position of the Gaussians in the expression branch, which allows us to perform fast reenactment without redundant calculations.Reconstructing 3D Gaussians typically requires millions of points, but in our dual-lifting method, we generate only 175,232 points. These Gaussians can reconstruct the target avatar, but with RGB information alone it is insufficient for capturing the rich details of a human avatar. To enhance the representation capability of the sparse Gaussians, we predict 32-dimensional features containing RGB information and then perform splatting to obtain coarse images. Then we use a popular neural renderer following existing methods [Li et al., 2023a , Chu et al., 2024 , Ye et al., 2024] to get the fine image, as Fig. 2 shows. Unlike these methods which use neural render as a super-resolution module to reduce rendering time, we do not upsample the image as our method do not face significant rendering time issues. Our neural renderer effectively decodes the dual lifting and expression Gaussians features into RGB values, producing high-quality results and resolving potential conflicts between the two sets of Gaussians. We train our neural renderer from scratch during the training process, without any pre-trained initialization.With the exception of the frozen DINOv2 backbone, we train the model from scratch. During training, we randomly sample two images from the same video, one as the source image and the other as the driving image and target image. Our primary objective during training is to ensure that the reenacted coarse and fine image aligns with the target image. Given that both images share the same identity, this alignment is achievable. We employ L1 loss and perceptual loss [Johnson et al., 2016 , Zhang et al., 2018 , Ye et al., 2024] on both the coarse and the fine image.Additionally, we propose a lifting distance loss L lif ting to assist dual-lifting learning. With the help of the prior provided by the tracked 3DMM, we require the lifting distance predicted by the network to be as close as possible to the 3DMM vertices. Specifically, we look for the lifting point closest to each 3DMM vertex and constrain their distance through L2 loss. The calculation is as follows:EQUATIONwhere the P 3dmm is the tracked 3DMM vertices, G pos is the dual-lifting points, argmin find the nearest point. Our lifting distance loss leverages 3DMM priors. Additionally, since we constrain only The overall training objective is as follows:EQUATIONwhere I t is target image, I c and I f are the generated coarse and fine image, λ p and λ l are the weights used to balance the losses.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer: [NA] Answer: [Yes] Justification: We have included the core code in the supplementary material and will release the full code after refactoring cleanup. Our data uses publicly available data.Guidelines:• The answer NA means that paper does not include experiments requiring code. • Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
experiment setting: 
 Datasets. We use the VFHQ [Xie et al., 2022] dataset to train our model, which comprises clips from various interview scenarios. To avoid consecutive similar frames, we sampled 25 to 75 frames from the original video depending on video length. This resulted in a dataset that includes 586,382 frames from 15,204 video clips. All the images are resized to 512×512. We tracked camera poses, FLAME [Li et al., 2017] parameters and removed the background following [Chu et al., 2024] . For evaluation, we use sampled frames from the VFHQ original test split, consisting of 5000 frames from 100 videos. The first frame of each video serves as the source image, with the remaining frames used [Khakhulin et al., 2022] 19.96 0.786 0.192 0.701 0.138 0.186 4.986 0.530 0.259 0.277 OTAvatar [Ma et al., 2023] 17.65 0.563 0.294 0.465 0.234 0.545 18.19 0.364 0.324 0.678 HideNeRF [Li et al., 2023b] 19.79 0.768 0.180 0.787 0.143 0.361 7.254 0.514 0.277 0.527 GOHA [Li et al., 2023a] 20.15 0.770 0.149 0.664 0.176 0.173 6.272 0.518 0.274 0.261 CVTHead [Ma et al., 2024a] 18.43 0.706 0.317 0.504 0.186 0.224 5.678 0.374 0.261 0.311 GPAvatar [Chu et al., 2024] 21.04 0.807 0.150 0.772 0.132 0.189 4.226 0.564 0.255 0.328 Real3DPortrait [Ye et al., 2024] 20.88 0.780 0.154 0.801 0.150 0.268 5.971 0.663 0.296 0.411 Portrait4D [Deng et al., 2024b] 20 [Li et al., 2023b] 21.08 0.811 0.117 0.858 0.120 0.247 5.837 0.843 0.276 0.288 GOHA [Li et al., 2023a] 21.31 0.807 0.113 0.725 0.162 0.117 6.332 0.735 0.277 0.136 CVTHead [Ma et al., 2024a] 20.08 0.762 0.179 0.608 0.169 0.138 4.585 0.591 0.242 0.203 GPAvatar [Chu et al., 2024] 23.06 0.855 0.104 0.855 0.114 0.135 3.293 0.842 0.268 0.219 Real3DPortrait [Ye et al., 2024] 22.82 0.835 0.103 0.851 0.138 0.137 4.640 0.903 0.299 0.238 Portrait4D [Deng et al., 2024b] 20.81 0.786 0.137 0.810 0.134 0.131 4.151 0.793 0.291 0.240 Portrait4D-v2 [Deng et al., 2024a] as driving and target images for reenactment. We also evaluate on HDTF [Zhang et al., 2021] dataset, following the test split used in [Ma et al., 2023 , Li et al., 2023a] , including 19 video clips.Implementation details. Our framework is built on the PyTorch [Paszke et al., 2017] platform. We use FLAME [Li et al., 2017] as our driving 3DMM. During training, we use the ADAM [Kingma and Ba, 2014] optimizer with a learning rate of 1.0e-4. The DINOv2 [Oquab et al., 2023] backbone is frozen during training and is not trained or fine-tuned. Our training consists of 200,000 iterations with a total batch size of 8. The training process is conducted on an NVIDIA Tesla A100 GPU and takes approximately 46 GPU hours, demonstrating efficient resource utilization. During inference, our method achieves 67 FPS on an A100 GPU while using only 2.5 GB of VRAM, showcasing high efficiency. Further implementation details of the model can be found in the supplementary materials.Baseline methods. We conduct comparisons with existing state-of-the-art methods, including ROME [Khakhulin et al., 2022] , StyleHeat [Yin et al., 2022] , OTAvatar [Ma et al., 2023] , HideN-eRF [Li et al., 2023b] , GOHA [Li et al., 2023a] , CVTHead [Ma et al., 2024a] , GPAvatar [Chu et al., 2024] , Real3DPortrait [Ye et al., 2024] , Portrait4D [Deng et al., 2024b] , and Portrait4D-v2 [Deng et al., 2024a] . For each method, we use the official implementation to obtain the result. It is worth noting that actually the core contributions of Portrait4D-v2 are orthogonal to our work. They introduced a new data generation method and a novel learning paradigm to improve performance, which means our method can also benefit from their advancements.Qualitative results. Fig. 3 shows qualitative comparisons between methods. Compared with other methods, our method can reconstruct detailed head avatars from source images and capture subtle facial movements such as eyes and mouth in driving images. Our method can also maintain identity consistency and image quality when handling large head rotations. At the same time, our method achieves high-quality reconstruction and rendering at a much faster speed than the baseline method.Quantitative results. We also quantitatively evaluate the self and cross-identity reenactment performance between methods. For self-reenactment with ground truth available, we measure the quality of the synthesized images using PSNR, SSIM, LPIPS [Zhang et al., 2018] between the synthetic results and the ground truth. For identity similarity, we calculate the cosine distance of face recognition features [Deng et al., 2019a] between the reenactment results and the source images. For expression and pose, we use the average expression distance (AED) and average pose distance (APD) measured by a 3DMM estimator [Deng et al., 2019b] , and the average keypoint distance (AKD) based on a facial landmark detector [Bulat and Tzimiropoulos, 2017] to evaluate the accuracy of driving control.For the cross-identity reenactment task, due to the lack of ground truth, we evaluate CSIM, AED, and APD, generally consistent with previous work [Li et al., 2023a , Chu et al., 2024 , Ye et al., 2024] .Tab. 1 and Tab. 2 show the quantitative results on the VFHQ and HDTF datasets, respectively. Our method outperforms previous methods in terms of reconstruction and synthesis quality and expression control accuracy but the cross-reenactment identity consistency is slightly worse than some existing methods. We believe this is due to the 3DMM [Li et al., 2017] and 3DMM tracker we rely on, whose identity parameters and expression parameters are not completely decoupled. Some methods [Deng et al., 2024b,a] that are not based on 3DMM have brought some inspiration to solve this limitation, and we leave these to future work. Importantly, our model not only achieves these quantitative results, but also achieves the real-time reenactment speed, which is much faster than existing methods.Inference speed and efficiency. Our method can run at 67 FPS on an A100 GPU with the naive PyTorch framework and official 3D Gaussian Splatting implementation. As shown in Tab. 3, we are the first real-time method for animatable one-shot head avatar reconstruction, which shows the application prospects and unique value of our method. Dual-lifting. To validate the effectiveness of our proposed dual-lifting method, we compare it against a baseline that lifts points from a single plane. This baseline requires the model to simultaneously lift points forward and backward from the image plane, sometimes creating ambiguities. The results in Tab. 4 and Fig. 4 show that dual-lifting significantly enhances reconstruction quality. Moreover, since the lifting is performed only once per identity and subsequent expression driving does not require recalculation, dual-lifting does not impact the performance during reenactment. Lifting distance loss. We evaluate the influence of the lifting distance loss L lif ting by removing it during training. Without lifting distance loss, we observed performance degradation as shown in Tab. 4 and Fig. 4 . Compared with our full method, removing the point distance constraint will make it more difficult to reconstruct high-quality 3D structures, especially on facial edges.3D structure of dual-lifting. We further analyze and compare the 3D structure of dual-lifting. We show the visualization of filtered lifting points in Fig. 5 . It can be seen that in the case of single-plane lifting or without L lif ting , the model can learn the correct 3D lifting even without any explicit 3D constraints. However, dual-lifting can produce 3D Gaussian points away from the input angle, and the 3D structure is also more reasonable rather than relatively flat.Global feature in expression branch. We conduct an ablation study by removing the global identity features F id from the expression branch. The results in Tab. 4 and Fig. 4 indicate that removing F id decreases the identity similarity (CSIM) of the results and the reenactment quality. This demonstrates the importance of incorporating identity information in the expression branch.Neural renderer. Due to the sparsity of our reconstructed Gaussians, we increased the output dimensions and introduced a neural renderer to refine the coarse images and features. This process is similar to the super-resolution module in EG3D [Chan et al., 2022] , but our neural renderer does not increase the resolution of the results. The results in Tab. 4 and Fig. 4 show the performance of coarse results without neural rendering. It can be observed that we can obtain reasonable results even using only sparse Gaussians, but the neural renderer significantly improves detail and expression.Extreme inputs. We present more qualitative results with extreme inputs in Fig. 6 . For extreme expressions or common occlusions such as sunglasses, our method shows good robustness. Our model can also work well with low-quality images and challenging lighting conditions, but the details of reconstructed avatars are inevitably affected. For example, avatars reconstructed from blurred images lack details, while those from images with challenging lighting conditions have fixed lighting, such as shadows on the nose. However, these features also demonstrate that our method can faithfully restore details and handle various extreme cases.Resolve conflicts of dual-lifting and expression Gaussians. Although we attempt to bring the two sets of Gaussians closer, there are inherent conflicts since one set is static and the other is dynamic.We show some results with conflicts in Fig. 7 . It can be seen that the RGB values conflict when there is a significant expression difference between the dual-lifting Gaussians and the expression Gaussians, but these conflicts are well resolved after neural rendering. We believe this is because our Gaussians have 32-D features that contain more information than RGB values. The neural rendering module can act as a filter to integrate the two point sets using these features and resolving possible conflicts.Figure 6 : The robustness of our model. Our method can produce reasonable results for low-quality images, challenging lighting conditions, significant occlusions, and extreme expressions.Figure 7 : The case where two sets of Gaussians conflict, the conflict is resolved after neural rendering. We believe that neural rendering resolves the conflict through the 32D features carried by Gaussians. 
conclusion: 
 In this paper, we proposed a novel framework for one-shot head avatar reconstruction and realtime reenactment. The key innovation of our method is the dual-lifting approach for one-shot 3D Gaussian reconstruction, which estimates the Gaussian parameters in a single forward pass. We also propose a 3DMM-based expression control method and a loss function that uses 3DMM priors to constrain the lifting process. Our experiments demonstrate that our method outperforms state-ofthe-art baselines in both the quality of head avatar reconstruction and reenactment accuracy, with significant improvements in rendering speed. We believe our method has a wide range of potential applications due to its strong generalization capabilities and real-time rendering speed.Broader impacts. Although our method has great potential in various applications, it also poses the risk of misuse, such as generating fake videos and spreading false information. We strongly oppose such misuse and have proposed several measures to prevent it, as detailed in Sec. E. With proper and responsible use, we believe our method can offer significant benefits in a wide range of applications such as video conferencing and entertainment industries.Limitations and future work. Despite its strengths, our method has certain limitations. For example our model may generate less detail for unseen areas, and our 3DMM-based expression branch cannot control the areas not modeled by 3DMM, such as hair and tongue. These limitations highlight the possible improvements in future work to increase the performance and practicality of our method. In Sec. F, we provide a more detailed discussion of our limitations and future work. that cannot be explained solely by linear blend skinning, and expression blend-shapes BE( ψ; E) is used to capture facial expressions such as closing eyes or smiling. 
experimental setting/details: 
 Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?Answer: [Yes] Justification: We describe in the implementation details section.Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
experiment statistical significance: 
 Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?Answer: [No] Justification: We use fixed random seeds in all experiments to ensure reproducibility.Guidelines:• The answer NA means that the paper does not include experiments.• The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper. • The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions). • The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.) • The assumptions made should be given (e.g., Normally distributed errors).• It should be clear whether the error bar is the standard deviation or the standard error the mean. • It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified. • For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates). • If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text. 
experiments compute resources: 
 Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?Answer: [Yes] Justification: We describe in the implementation details section.Guidelines:• The answer NA means that the paper does not include experiments.• The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage. • The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute. • The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). 
code of ethics: 
 Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines?Answer: [Yes] Justification: We fully follow the NeurIPS Code of Ethics.Guidelines:• The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.• If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction). 
broader impacts: 
 Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?Answer: [Yes] Justification: We discuss it in a broader impacts section.Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.• The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?Answer: [Yes] Justification: We discuss it in the broader impacts section.Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?Answer: [Yes] Justification: It is included in the source code.Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. • For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. 
 
 Appendix: We present the results of rendering the dual-lifting Gaussians from the reconstruction branch and the Gaussian from the expression branch separately. As Fig. 9 shows, the dual-lifting Gaussians reconstruct the head's base structure and facial details respectively, which is in line with our expectations. We also show more lifted points in Fig. 10 , we can see that the dual-lifting point cloud has rich details, including glasses and hair. Additionally, we provide some lifting point cloud files in supplementary materials.We show more self-identity qualitative comparisons with baseline methods in Fig. 11 , and crossidentity qualitative comparisons in Fig. 13 . Here we show the results of all baseline methods on the VFHQ [Xie et al., 2022] dataset and HDTF [Zhang et al., 2021] dataset.We also show more results of our method and baseline methods for self and cross-identity reenactment. In Fig. 12 , we not only show the reenactment results but also the multi-view results of our method. In Fig. 16 , we show more comparisons and consecutive frames and highlight the regions of interest. We also show more in-the-wild results of our method in Fig. 8 , 14 and 15. It can be seen that our method maintains good identity consistency and 3D consistency when the viewing angle changes.Additionally, we provide a supplementary video to demonstrate video driving results. Although no special processing is performed, our method has timing-stable results on video generation. Figure 13 : Cross-identity reenactment results on VFHQ [Xie et al., 2022] and HDTF [Zhang et al., 2021] datasets. The top ten rows are from VFHQ and the bottom four rows are from HDTF.Our framework offers many applications but also presents ethical risks, such as the potential creation of fake videos ("deepfakes"), violations of privacy, and the dissemination of false information. We do not advocate such misuse and have proposed several measures to prevent these risks:Watermarking technologies. To ensure transparency and prevent misuse, we plan to employ watermarking techniques in code that will be released. Visible watermarks enable viewers to immediately recognize content as AI-generated, helping them distinguish potential misuse. In addition to visible watermarks, we plan to embed invisible watermarks [Tancik et al., 2020] , which are difficult to remove. These invisible marks help track and identify the source of videos, even if they are re-edited. This tracking capability encourages producers to consider the ethical implications and potential risks of their creations by storing information about the video producer.Strict licenses. We will release our code and model under a strict license. The license will prohibit the synthesis of real individuals without explicit consent for commercial use. This ensures that our Figure 17 : Our model has some limitations. For example, the tongue is not modeled and the unseen regions of the input have less details. Better to view it zoomed in.technology is used ethically and prevents it from violating the consent of the individual represented by the avatar. Illegal misuse can be traced through the watermark system.In summary, we will implement robust safeguards to prevent the misuse of our head avatar reconstruction system. We urge video creators to be mindful of the ethical responsibilities and potential risks when using talking face generation technologies. With careful and responsible use, our method can provide substantial benefits across various real-world applications.Although our method achieves high-quality synthesis results compared to previous approaches, there are still some limitations. When rendering synthesized results from novel views, unseen areas in the original source image often lack detail and may produce results with statistically average expectations.For example, generating the other half of the face from a side view input or generating an open mouth from an input image with a closed mouth. Additionally, our expression branch is based on 3DMM and learned from VFHQ video data. This branch may not capture extreme facial movements or parts not modeled by 3DMM, such as one eye being open and the other being closed, the tongue, and hair. We show the qualitative results of these limitations in Fig. 17 . Future work may involve learning expression embeddings [Deng et al., 2024b] directly from images, alleviating data requirements and tracking accuracy needs through data generation [Deng et al., 2024a] , gathering more expressive data to improve expression imitation. Extending our approach to handle full-body avatar synthesis is also a promising direction for future research.NeurIPS Paper Checklist38th Conference on Neural Information Processing Systems (NeurIPS 2024). 


Decision: This paper proposes a method for one-shot 3D reconstruction of faces from single input images along with their animation using input FLAME expression parameters. The authors propose a dual Gaussian lifting framework to lift a static head and the expression coefficients into a Gaussian representation. Additionally for the static branch, a backwards and forwards lifting approach is proposed, which is novel. A neural render is further proposed to resolve any discrepancies between the dynamic and static branches. The proposed approach also relies heavily on the parametric 3DMM model for regularizing 3D reconstruction. The proposed algorithm is compared to several existing state of the art baselines on two benchmark datasets and shown to be better in terms of reconstruction accuracy and expression preservation, but not SOTA in terms of identity preservation. By using Gaussian splatting the proposed method is also fast in terms of training and inference times versus the existing approaches. Lastly, without requiring test-time fine-tuning to new subjects the method generalizes out of the box to unseen faces.

The reviewers appreciated the novelty of the proposed approach in being the first to employ Gaussian splatting for generalized one-shot animatable avatar creation, its speed, generalization ability and the quality of the results. However, reviewers raised concerns about the heavy reliance on a 3DMM, which does not allow for parts of the face that aren't mapped to the 3DMM (hair and tongue) to be animatable. Requiring FLAME fitting, also makes identity and expression parameters to not be disentangled fully. Reviewers also raised concerns about the design choice of employing the dual independent dynamic and static branches both if which predict Gaussians, which could lead to differing shapes and hence is not an intuitive design. 

All things considered, the AC feels that the proposed approach is a step in the right direction towards employing 3D Gaussians for generalized animatable 3D head avatar creation.While not perfect, it would form a strong baseline for future work to build upon and encourage further exploration and improvements w.r.t. moving away on the reliance on 3DMM. The authors have also promised to release their code. Hence, the AC leans towards accepting this work.

Reviews: review: paper_summary: paper_summary: This paper presents "Generalizable and Animatable Gaussian Head Avatar" (GAGA), a method for one-shot animatable head avatar reconstruction using 3D Gaussians. Unlike existing methods that depend on neural radiance fields and require extensive rendering time, GAGA employs a dual-lifting method to generate high-fidelity 3D Gaussians from a single image in a single forward pass. The approach integrates global image features and 3D morphable models to control expressions and achieve real-time reenactment speeds. Experiments demonstrate that GAGA outperforms previous methods in reconstruction quality and expression accuracy.
strength_weakness: strength_weakness: 1. The reliance on 3D morphable models for expression control may limit the model's flexibility.
2. The 3DMM identity and expression parameters are not entirely decoupled, potentially impacting cross-identity reenactment identity consistency.
3. The method may generate less detail for unseen areas and has limitations in controlling regions not modeled by 3DMM, such as hair and tongue.
questions: questions: 1. How sensitive is the model to variations in the input image quality and lighting conditions?
2. How robust is the model when applied to images with significant occlusions or extreme facial expressions?
3. What specific measures were taken to ensure the diversity and quality of the training data?
limitations: limitations: 1. The model may produce less detail for unseen areas, which can affect the realism of the generated avatars in dynamic scenes.
2. The expression control is limited by the constraints of the 3DMM, which does not model elements like hair and tongue, leading to potential inaccuracies.
3. There is a slight compromise in identity consistency of cross-identity reenactment due to the partial decoupling of identity and expression parameters in the 3DMM.
4. While the method achieves real-time speeds, the dual-lifting and neural rendering processes add complexity to the implementation and may require significant computational resources for training.
paper_summary: paper_summary: The paper introduces a novel framework, GAGA, for one-shot animatable head avatar reconstruction from a single image. The key innovation is the dual-lifting method, which generates high-fidelity 3D Gaussians that capture identity and facial details by predicting lifting distances from the image plane to 3D space. This method leverages global image features and a 3D morphable model (3DMM) to ensure accurate expression control and real-time reenactment speeds. The model can generalize to unseen identities without specific optimizations. Experimental results demonstrate that GAGA outperforms previous methods in terms of reconstruction quality and expression accuracy. The main contributions include the introduction of the dual-lifting method, the use of 3DMM priors to constrain the lifting process, and the combination of 3DMM priors with 3D Gaussians for efficient expression transfer, allowing high-quality real-time rendering.
strength_weakness: strength_weakness: - The Gaussians from the expression branch only use the vertices of the 3DMM model, which can be inaccurate and unable to model fine facial features.
- It would be nice to show more side-by-side video comparison with baselines. Many baselines look similar qualitatively and it’s hard to tell the improvement without highlighting the difference.
questions: questions: - Why not also predict position offsets for the expression Gaussians? To keep expression structures, regularization can be used.
- Would the two sets of Gaussians (expression & dual-lifted) contract each other during expression?
- Why not bind the lifted Gaussians to the 3DMM vertices and animate together?
limitations: limitations: The paper acknowledges several limitations. First, the model may generate less detailed reconstructions for unseen areas of the face, such as the back of the head or the interior of the mouth, which are not visible in the input image. Second, the 3DMM-based expression branch cannot control parts of the head that are not modeled by 3DMM, such as hair and the tongue.
paper_summary: paper_summary: The paper proposes a method to achieve one-shot head avatar animation with 3D Gaussian Splatting (3DGS). With 3DGS, the papers show high-fidelity animation with fast inference speed. To solve one-shot 3DGS reconstruction, the paper proposes a dual-lifting method with 3DMM regularization. Experiments justify the model designs and show the proposed approach achieves SOTA performance.
strength_weakness: strength_weakness: 1. The visual results of the method seem to be overly smoothed to some extent, making the results less realistic compared to some baselines (e.g., Portrait4Dv2). The authors may need to provide additional explanation for this and discuss why the method achieves better qualitative results than other methods.
2. The approach is highly dependent on 3DMM but lacks a discussion about its impact. What if the estimation fails? How does the estimation accuracy affect the final performance (both training and inference)? If using other designs without 3DMM, what would be the method's performance?
3. There should be some failure cases to show the limitations of the work.
questions: questions: 1. The current ablation study may not be sufficient to validate the dual-plane lifting. Is it possible to conduct a study by removing the reconstruction branch to better demonstrate the importance of dual-plane lifting?
limitations: limitations: Yes, the authors discuss their "Limitations" and provide an "In-Depth Ethical Discussion" proposing several measures to prevent these ethical risks.
paper_summary: paper_summary: This paper presents a method for animatable facial avatar reconstruction from a single RGB image, and the reconstructed avatar is based on 3D Gaussian splatting (3DGS) to support real-time rendering. To this end, the proposed method generates pixel-aligned Gaussian point clouds to reconstruct the identity, and use 3DMM to construct an additional set of Gaussian points to control the facial expression. The network is trained on a large scale corpus of human talking videos. After training, the network can produce reenactment results at real-time framerate for any unseen identities. Experiments show that the proposed method is able to produce plausible reenactment results.
strength_weakness: strength_weakness: * My major concern is about the method design. Despite the plausible results, the proposed method is confusing and doesn't make sense to me. If I understand correctly, the reconstruction branch produces Gaussian points that are aligned with the input image, and these points keep static and unchanged when performing reenactment. Instead, the expressions are modeled through the additional Gaussian points that are attached to the 3DMM surface (Line 187-188), which means these expression Gaussian points should play a major role in expression control. However, according to Figure 7, the Gaussian points produced by the reconstruction branch actually play a major role in facial rendering, while the "expression Gaussians" seem to be much less important. That doesn't make sense to me: how could the static points produced by the reconstruct branch model the dynamic expressions? When overlapping the reconstruction Gaussian points with the expression ones, how does the proposed method resolve the potential conflicts of these two point sets, such as different lip positions?


* The so-called dual-lifting technique looks straight-forward and trivial to me. According to Sec.3.1, this module produces two pixel-aligned Gaussian point sets, one for the visible surface and the other for the invisible surface. However, using two set of points (visible+invisible, or front+back) to model a complete shape is not a new idea; it has been used in 3D human reconstruction like [a] many years ago. Although such an idea has not been proposed in the form of 3DGS, I don't think it is novel enough to be claimed as a technical contribution. 

* It is unclear whether the baseline methods are trained on the same dataset as the proposed method or not. In Line 244 of the main paper, the authors mention that they "use the official implementation", but it is still unclear whether the authors use the pretrained network weights or retrain these baselines using the same dataset.

[a] Gabeur et al. Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images. ICCV 2019.
questions: questions: One minor suggestion: it would be better if the authors could provide some qualitative comparison against state-of-the-art methods in the form of dynamic reenactment videos and highlight the difference/advantages of the results.
limitations: limitations: The authors have discussed the limitation and potential social impact in Sec.5 of the main paper and Sec.E & F of the supplemental document.

Comments: title: Response to Authors
Thanks for the rebuttal.
Author Rebuttal
Thank you
comment: Dear Reviewer d3t7,

The authors have provided a response to the questions you raised in your rebuttal. The discussion period with them is soon coming to a close, in less than 24 hours at 11:59 PM AOE Aug 13, 2024. It would be great if you could acknowledge that you've read the authors response, engage in any further discussion with them and update your score if needed.

Best,
AC
Thank you for your thoughtful feedback and for taking the time to review our rebuttal and engage with us. Your comments are important for us. We are pleased we could address your concerns.

We are happy to address any further concerns.
Thank you for your thoughtful feedback and for taking the time to review our rebuttal and engage with us. Your comments are important for us. We are pleased we could address your concerns.

We are happy to address any further concerns.
Thank you for the rebuttal. After carefully reviewing the comments from the other reviewers and the authors' responses, I have decided to maintain my rating of borderline accept.
Thank you for the rebuttal. After reading the comments from other reviewers and the reponses from authors, I would keep my rating as borderline accept.
Thank you for taking the time to review our ethical issues. We are pleased that our paper doesn’t raise ethical issues.

We are happy to address any further concerns.
Thank you for taking the time to review our ethics issues. We are pleased our paper could address your concerns.

We are happy to address any further concerns.
We thank the reviewer for their thoughtful feedback and for taking the time to review our rebuttal and engage with us. Your comments are important for us. We are pleased we could address your concerns.

We are happy to address any further concerns.
Dear Reviewers,

The authors have provided detailed responses to the questions that you raised. I strongly encourage you to please read the authors' responses and acknowledge that you have read them before the AOE 11:59 PM (Aug 13, 2024).

Best,
AC
Thank you for the detailed response and additional experiments. I will maintain my initial score of weak accept.