FastGHA: Generalized Few-Shot 3D Gaussian Head Avatars with Real-Time Animation
Authors: Xinya Ji
Sebastian Weiss
Manuel Kansy
Jacek Naruniec
Xun Cao
Barbara Solenthaler
Derek Bradley
Venue: ICLR 2026 Poster (2026)
Keywords: Animation
Gaussian Avatar
Feedforward Gaussian Model
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A new method for creating high quality 3D Gaussian Head Avatars from a few input images, which allows real-time dynamic animation.

Abstract: Despite recent progress in 3D Gaussian-based head avatar modeling, efficiently generating high fidelity avatars remains a challenge. Current methods typically rely on extensive multi-view capture setups or monocular videos with per-identity optimization during inference, limiting their scalability and ease of use on unseen subjects. To overcome these efficiency drawbacks, we propose FastGHA, a feed-forward method to generate high-quality Gaussian head avatars from only a few input images while supporting real-time animation. Our approach directly learns a per-pixel Gaussian representation from the input images, and aggregates multi-view information using a transformer-based encoder that fuses image features from both DINOv3 and Stable Diffusion VAE. For real-time animation, we extend the explicit Gaussian representations with per-Gaussian features and introduce a lightweight MLP-based dynamic network to predict 3D Gaussian deformations from expression codes. Furthermore, to enhance geometric smoothness of the 3D head, we employ point maps from a pre-trained large reconstruction model as geometry supervision. Experiments show that our approach significantly outperforms existing methods in both rendering quality and inference efficiency, while supporting real-time dynamic avatar animation.

FASTGHA: GENERALIZED FEW-SHOT 3D GAUSSIAN HEAD AVATARS WITH REAL-TIME ANIMATION
Xinya J Sebastian Weiss", Manuel Kansy?, Jacek Naruniec", Xun Cao', Barbara Solenthaler", Derek Bradley" Nanjing University, "ETH Zürich, Resenyye (arrssaarr//SSuoooss
ABSTRACT
Despite recent progress in 3D Gaussian-basee head avatar modeling, efficiently generating high fidelity avatars remains a challenge. Current methods typically rely extensive multi-view capture setups monocular videos with per-identity optimization during inference, limiting their scalability and ease of use on unseen subjects. To overcome these efficiency drawbacks, we propose FastGHA, a feed- forward method to generate high-quality Gaussian head avatars from only few input images while supporting real-time animation. Our approach directly learns a per-pixel Gaussian representation from the input images, and aggregates multi-view information using a transformer-base based encoder that fuses image features from both DINOv3 and Stable Diffusion VAE. For real-time animation, we extend the explicit Gaussian representations with per- Gau-Gansiann features and introduce lightweight MLP-based dynamic network to predict 3D Gaussian deformations from expression codes. Furthermore, to enhance geometric smoothness of the 3D head, we employ point maps from pre- trained large reconstruction model as geometry supervision. Experiments show that our approach significantly outperforms existing methods in both rendering quality and inference efficiency, while supporting real-time dynamic avatar animation.
INTRODUCTION
Over the last few years the field of digital avatars has seen tremendous growth, fueled primarily by the advent of neural rendering (Mildenhall et al., 2021 Lombardi et al. 2019) and 3D) Gaussian Splats (3DGS) (Kerbl et al.] 2023) in particular. The simple parameterization, tractable reconstruction, and real-time novel view rendering of 3DGS provides suitable representation for photoreal digital human avatars. For this reason, Gaussian-based avatars have been extensively explored in several
recent works (Kirschstein et al. 2025 Gong et al. 2025 He et al. 2025 Chu & Harada 2024).
Initial digital avatar methods require synchronized multi-view video or long monocular data for the subject to be reconstructed (Hong et al., 2022. Qian et al. 2024; Giebenhain et al.] 2024; Wang ct al., 2025a Zielonka et al.) 22255 Ma al. 2024). While this approach proved the ability to represent digital avatars using Gaussian-based representations, this avenue is clearly not scalable. Some works reduce the data requirements by synthesizing novel views first and then optimizing
Gaussian avatar on these synthetic observations (Taubner et al.) 2025 Zhou et al. 2025 Tang et al.) 2025 Yin et al.) 2025 Gu al. 2025). While this alleviates the need for dense captures, it still requires long per-identity reconstruction. Another line of research involves training generative models with 3D head priors. but still requires fitting an identity code for each new person at inference (Zheng cet al, 2025: Sun et al. 2023 Tang et al., 2023; Xu et al., 2025). The most efficient and attractive approach that has emerged so far is to build the avatar in a forward way given only small set of images of the subject. While this requires pre-training a generalized model on large corpus of human face data, enables instantaneous reconstruction and novel-view rendering of unseen subjects at inference time. Inspired from the success of large reconstruction models (Xu al.) 2024a), recent work such as Avat3r (Kirschstein et al. 2025) and Facelift (Lyu et al. 2024) leverage a forward network to fuse sparse input views into 3D Gaussian head representations. However, these methods either lack support for controllable facial animation or suffer from slow animation speed and limited reconstruction fidelity.

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Pbuax Scaze
Phone Stans
Expresioon
Diftusion
Expressions
Figure 1: We present FastGHA, feed-forward method that generates Gaussian head avatars from few-shot input images and animates them in real time.
In this work, we propose FasIGHA, a new architecture for generalized few-shot 3D Gaussian head avatar reconstruction that allows real-time animation. Our network is ferd-forward approach that takes only a few images of a subject from well-distributed views and arbitrary expressions and learns high quality animatable avatar. We infer per-pixel Gaussian representation, enabling the preservation of fine-grained head details. To achieve high-fidelity and efficient animation, we adopt two-stage pipeline first reconstruct a canonical Gaussian head from the input images, followed by lightweight deformation network to animate the Gaussians based on descriptive expression codes. In this way, our model learns disentangle the different facial expressions in the input images by effectively mapping the images to a neutral canonical Gaussian head before applying animation. For high-quality reconstruction, we leverage features extracted from a pre-trained diffusion VAE (Rombach al. 2022) and DINOv3 (Siméoni et al. 2025), and introduce transformer-basee feed-forward module to fuse multi-view information from these features. For real-time animation, we extend the explicit Gaussian representation with per- per-Gaussian features and introduce lightweight MI.Pbased dynamic network to predict fine-scale Gaussian deformations from expression codes. Furthermore, to enhance geometric smoothness of the 3D head and the robustness of model, we employ point maps predicted from a pre-trained transformer (Wang et al.) 2023b) as geometry supervision. We train the network large scale multi-view head video datasets in end-to-end way. Our method allows real-time animation of Gaussian head avatars created in feed-forward manner, with reconstruction in less than one second. Extensive experiments also show that FastGHA outperforms Avat3r and other state-of- state-of-the-art approaches in both reconstruction fidelity and animation efficiency.
Specifically, we offer the following contributions:
We propose FasGHHA, a framework for generalizable 3D Gaussian head avatar reconstruction from few-shot images with measurable quality improvement over current ttate-of-the-att methods;
We achieve real-time animation by introducing a lightweight MLP conditioned on extended per-Gauee Ger-Gaussian features to learn fast pixel-wise 3D Gaussian deformations;
We formulate a geometry prior from VGGT as regularization loss during training to improve the 3D head consistency and robustness.
2 RELATED WORK
Animatable 3D Head Avatars. Generating animatable 3D head avatars from few input images
a challeging task that typically requires accurate head geometry reconstruction and a robust method for capturing dynamic expressions. Early works (Khakhulin al. 2022 Xu et al.) 2020 rely on parametric face models such as 3DMM to reconstruct and animate human heads. These methods are limited by rendering quality and fail produce realistic animation. With the advent of
2

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Neural Radiance Fields (NeRFs) and subequent 3D Gaussian Splatting (3DGS), high-quality avatar
reconstruction with real-time rendering became feasible. Most approaches (Gafni et al.] 2021 Gao
et 2022 Hong et al.
2022 Ki et al. 2024;
Ma et al. 2024: Xu et al. 2024b] Qian al. [2244]
Giebenhain et al., 2024 Dhamo et al. 2024 Tretschk et al. 2021] Wang et al. 2025a Zielonka
(et a 2025) require expensive training
long monocular or multi-view video sequences for
each subject, making them impractical in many scenarios. Some methods (Taubner et al.) [025
Zhou al. 2025 Tang et al., 2025 Yin et al. 2025; Gu et al.) (025) bypass the extensive data requirement, by first generating novel-views from a limited set of images, and then optimizing a
Gaussian avatar using these synthetic observations. This approach, however, does not solve the long
optimization time for new characters. Another line of research (Zheng et al. [025 Sun et al.) 2023
Tang et al. 2023 Xu et al. 2025) leverages large-scale video datasets to learn 3D head priors by training generative avatar models. During inference time, an identity code is fitted to new, unseen person. This process is conescin,,,,ng thus restricting the set of possible application scenarios. Recently, several works (Kirschstein et al. 2025 Gong et al. [025 He et al. 2025 Chu & Harada 2024, Zhao et al. 2024 Chu et al. 2024) explored feedforward avatar reconstruction in a few- or single-shot setting. Most single-shot methods trained on large monocular video datasets (Gong et al.) 2025 He et al. 22255 Gafni et al. (021)) l) lack 3D consistency and focus primarily on the frontal view. Most recently, Avat3r (Kirschstein et al. 2025) introduced an animatable, feedforward Gaussian reconstruction model based on four arbitrary views. However, the inaccurate geometry predicted by general reconstruction model is directly injected into the network through skip-comnections. which negatively affects the reconstruction fidelity. Moreover, they use cross attention blocks to model facial animation from expression codes, resulting in more computation and slow rendering speed. Specifically, it is not possible to achieve real-time novel animation with their architecture. In this paper, we employ a similar feedforward paradigm as Avat3r (Kirschstein et al. 2025) and support an arbitrary number of input views. We use large reconstruction model, VGGT (Wang et al., 2025b), as a geometry prior during training, avoiding the error propagation during inference. We also design a lightweight MLP to model Gaussian deformations from expression codes, enabling real-time animation with high fidelity.
2D Facial Animation. In recent years, the development of deep generative models has led to rapid advancements of photoreal 2D facial animation methods. Early work (Qiao et al. 2018 Siarohin
Wang al., 2021 Tewari al. 2020) take GANs (Goodfellow et al. 2020 Karras et al. 2019 as the backbone for motion generation. Though computationally efficient, these methods lack diversity and suffer from poor generalization to occluded areas and restricted expressiveness for intricate facial dynamics. Recently, diffusion-bar based generative models (Song et al., 220 b Wang et al., 2024a) have demonstrated remarkable capabilities and superior quality in many image generation tasks. Large pre-trained models, such as Stable Diffusion (Rombach et al., 2022). have spurred numerous applications leveraging their robust model priors. Early works (Guo et al. 2024) Hu 2024; Xu et al., 2024c| Guan et al. 2024) explored the animation task by extending the pre- trained model from image generation to video generation by introducing temporal components.
eet al.
2019
2020
DreamPose (Karras et al. 2023) introduces dual clip-image encoder to animate person given set of input poses. Other methods (Guan et al. 2024 Xu et al. 2024c Hu 2024) resort to a ReferenceNet to generate full body animation based the rough joint estimates. Video diffusion models (Ho) let al. 2022) now emerged as backbone for high-quality facial animation, with explicit modeling of temporal correlations produce smooth and photorealistic videos from prompts or given driving frames. However, these methods (Cui et al. [2025] Blattmann et al., (2023; Yu et al.) (025) come with high computational cost, limiting their efficiency in real-time scenarios. Moreover, since they operate in the 2D domain without explicit 3D constraints, these methods struggle with geometric artifacts
under large view changes. In contrast, our approach reconstructs an explicit 3D Gaussian avatar,
which enables controllable animation under arbitrary viewpoints and expressions in real time.
3 FASTGHA METHOD
As illustrated in Figure 2. the goal of our model is to generate a high-fidelity and animatable head avatar from few-shot input views of target subject using dynamic 3D Gaussians controlled by expression codes (Section 3.1). We first reconstruct a canonical Gaussian point cloud from the input images and their camera parameters. Here, "canonical" means un un-posed Gaussian head with little to no facial expression, although we do not supervise this intermediate output. Note that the input images can portray any camera view or facial expression, as long the views
3

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Reconstraction
Animation Rendering
CCaccataatio
DINO
Beoader
2o Caustia Focace
Gaoede Daoseer
ViE
Mulli -iew Trandoomrr
Baonder
Emderig
ft
Cancnical Coumtto
Anicatal Gazwian
Rankeal Fazmo
Ieparrogge truges
Figure 2: Overview of our method. Given a few input images with arbitrary views and expressions, we first extract mueti-view features with pre-trained models and then train a multi-view transformer network that projects these features into 3D to reconstruct canonical Gaussian head avatar. To enable real-time animation, we introduce a lightweight MLP that deforms the Gaussians according to the expression code.
well-distributed, without requiring controlled multi-view capture system. Then, we introduce a lightweight multi-layer perceptron (MLP), which deforms the canonical Gaussians according to FLAME expression codes (Li et al., 2017) to handle the facial dynamics in real time. To achieve this feed-forward prediction, we train model large multi-view and multi-expression dataset using a combination photometric loss and geometric loss that employs a large foundation model
VGGT [Wang al., 2025b), see Section 3.2
3.1 ANIMATABLE GAUSSIAN HEAD AVATARS
3D Gaussian Representation. In 3D Gaussian Splatting (3DGS) (Kerbl et al. 2023). the scene is represented by a set of Gaussian ellipsoids G, each defined by its position X, multi-channel color C, rotation Q, scale and opacity a. Given camera parameters μr, these Gaussians can rasterized and rendered (collectively denoted as R) into novel-view images This process can be described as:
G =
I= P)
In order to faciliate the learning Gaussian dynamics driven by expression codes, we augment the default Gaussian representation with a per-eeeussaee feature f R2 Nx32.
GJ (X, (X,C
These additional features encode high-level semantic information that guide the update of Gaussian attributes during animation and are used below to deform the Gaussians for novel expressions.
Feature Extraction and Reconstruction. Different from person-specific optimization ap- proaches (Xu et al.] 2024b Giebenhain et al. 2024 Dhamo et al., 2024). our model aims for feedforward reconstruction enabling fast head avatar reconstruction. Thus, instead of using template Gaussians rigged to 3DMM (Li et al. [0177] Gerig et al. 2018) face template, we draw inspiration from the recent success of large reconstruction models (LRM) (Xu et al., 2024a| Zhang al. 2024b Tang et al.) 20244] Kirschstein et al. 2025) and directly predict Gaussian primitives for each pixel of the input images, see Figure 2 Reconstruction. Following the paradigm of LRM, we first extract pie-view features using pre-trained models, then apply Vision Transformer (ViT) to capture multi-view correspondence and decoder to regress Gaussian attributes.
Specifically, we adopt the VAE from SD-Turbo (Sauer et al.) 2024) for color features and DI- NOv3 (Siméoni et al. 2025) for semantic features to encode RGB images Lix V into low-dimensional latent features, where V represents the number of input images. Camera poses represented as Plücker ray maps similar to Zhang et al, 2002aa b). which takes the form of image-like 2D data. The pre-traine treitreined encoder weights meaningful features. For DINOv3, we apply extra convolutional layers to fuse the features derived from different layers and obtain single feature map Fdino that matches the spatial dimension of the
frozen during training to ensure stable and semantically
4

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VAE feature map Fenc- We refer the supplementary material for details on the different feature layers. Then, we concatenate the image features and the Plücker ray coordinates map Peam:
Fdinoi Fenci Peom] E RR (2xxCc++C+@)xx
where [: denotes channel wise concatenation, Ca. Ce denote the channel dimensions of the DINOv3 and VAE feature maps respectively, and H = = H/8, w = W/8. The concatenated latent V is fed into Multi-view Transformer consisting of multiple self-attention layers to integrate cross-view cues and model geometric correspondences. Here, we use an implementation similar to GRM (Xu al. (224a), where each self-attention layer operates on H W tokens and outputs a feature vector with the same length. The resulting latent tokens are then decoded via a modified SD-Turbo VAE decoder, where the input and output channels are expanded accommodate and regress the Gaussian attributes while keeping the pre-trained inner layers as initialization. During training, we fine-tune the entire decoder. The decoder outputs a set of Gaussian image maps that contain per-pixel 3D Gaussian parameters. We regress the map for each input view, where the 3D Gaussians represent the head in a learned canonical expression. By design, the canonical head is devoid of expression and thus does not correspond to any of the input expressions, allowing animation happen downstream. These maps are fused into single canonical 3D Gaussian head, Gj.
Gaussian Head Animation. To achieve real-time animation pixel-wise Gaussians, we leverage a Culti-layer-Perreptteee (MLP) D to model the facial dynamics based on FLAME expression parameters. Specifically, 'D takes FL.AME expression code Zeup of the desired target expression along with the canonical Gaussian head 9j including learned per-Gaussian features and predicts offsets 0g for the Gaussian attributes:
82 = D(G Bexp).
Specifically, we predict expression-specifee offsets to the position X and color C of the Gaussians. Note that D acts independently on each Gaussian point, enabling efficient and parallelizable deforma- tion. The deformed Gaussians can then be rendered to get the final image Touts alpha mask Mout and depth map D Dout (used for supervision) at arbitrary camera views through the differentiable rasterizer R.
3.2 LOsS FUNCTION
We supervise the training with photometric losses on set of novel view images (see Section 4.1 for details). Our losses include an RGB loss and SSIM loss:
LRGB = =oou Lgell2.
LSSIM = ( Igt).
(5)
We also use a perceptual loss (Zhang et al. 2018b Johnson et al. 2016) well as silhouette loss to encourage the 3D shape consistency:
Epere VGG Igt).
Ceal ||Mout Mgell1-
(6)
Recent work (Kirschstein et al. 2025 Shi et al.
2025 Jiang et al., 2025) has shown the benefit
of including a geometry prior predicted by a 3D geometry model (Wang al. 2025b 2024b) for Gaussian-based reconstruction. Therefore, we introduce a geometric loss derived from a pre-trained VGGT (Wang et al. 2025b) backbone. Different from Avat3r (Kirschstein et al.) 2025), which directly takes the predicted point map input, we incorporate the geometry prior as regularization loss during training. This approach alleviates the requirement of the network explicitly compensate for discontinuities and artifacts in the prior. To ensure the consistency between the geometry prior and our Gaussian avatar, we first align the VGGT point cloud with the ground truth 3D face mesh (more details are presented in the supplementary document). Then, the aligned points are reprojected by camera y to infer a depth map, which serves as the ground-truth Dgt. The geometric loss formulated as:
Egoo = Dout - Doell1-
(7)
Overall, the total loss function is formulated as:
L LIGB ASSIM + AperoCrere + AsaL + AgeaCgeas
(8)
where denoting the weights each term, and are set as follows: ASSIM = 1, Aoil = 1, Apere and Ageo 0.5.
0.5
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4 EXPERIMENTS
In the following, we start by describing our training data, implementation details and comparison baselines in [Section 4.1 We then demonstrate our results, comparisons and analysis in Section 4.2 Ablation studies that justify our main design decisions are given in Section 4.3) and we demonstrate applications of our method in Section 4.4
4.1 EXPERIMENTAL SETUPS
Dataset. Our model is trained on a combination of two multi-view video datasets, Ava-256 (Martinez et al. (024) and Nersemble (Kirschstein et al. (023). The Ava-256 dataset contains 256 subjects captured with 80 cameras, from which we use only 40 cameras with frontal viewpoints for training. The Nersemble dataset has total of 425 subjects with 16 cameras. For each video sequence, we first crop the image to 512 x 512 resolution, remove the background (Lin et al., (021) and then employ off-the-shelf head tracking tools (Zielonka et al. 2022 Qian et al. 2024) to obtain FLAME ) with jaw and eye poses as the per-frame expression codes Zeup- Similar to Kirschstein et al. (2025). in each training iteration, we randomly sample four images of the same subject with different expressions and camera poses as input to the network. We additionally sample eight supervision images with the same expression, which is potentially different from the four input images. From these eight supervision images, four match the input camera views and four are from novel views, provide multi-view and novel-expression supervision.
expression weights [i et al. (2017)
Implementation details. We train the network using the ADAM optimizer (Kingma & Ba 2014 with a learning rate of 5e -5 except for the MLP D, where we use a higher learning rate of 5e The training takes roughly four days for 400k steps on four H800 GPUs with batch size of on each GPU. During inference, the first reconstruction part of our network only needs to be performed once for given set of input images and takes less than one second. After that, our method achieves real-time animation and rendering on a single H800 GPU.
Baselines. We compare our approach with other state-of-the oof-the-an art methods including InvertAvatar (Zhao et al. 2024), GPAvatar (Chu al. 2024) and Avat3r (Kirschstein et al. 2025), which all reconstruct 3D head avatars from few-shot input images in single forward pass. InvertAvatar uses 3D GAN inversion for head avatar reconstruction. GPAvatar is based on the tri-plane representation (Chan et al.) 2022) and uses FLAME point cloud as prior to sample 3D points for animation. Avat3r is the most related to our work, as it directly regresses pixel-wise 3D Gaussians from the images. However, Avat3r incorporates the expression parameters directly into the cross-attention model for reconstruction, which comes at the cost of a slow animation speed of only 8 fps.
Metrics. To measure the image quality, we employ Peak Signal-to-Noise Ratio (PSNR), Struc- tural Similarity Index (SSIM) (Wang et al. al.] 2004) and Learned Perceptual Image Patch Similarity (LPIPS) (Zhang al. (018a) between the synthesized images and the ground truth images. We then use ArcFace (Deng et al. 2019) to compute the cosine similarity of the identity features for identity leverage Average Keypoint Distance (AKD) based on facial landmark detector (Jin et al. 2021 to evaluate the reconstruction and motion accuracy.
similarity (CSIM). Furthermore,
4.2
MAIN RESULTS
Quantitative Comparisons. We quantitatively evaluate our FastGHA method on a reconstruction task using held-out data from both the Ava-256 and Nersemble datasets. Results are shown in Tab] T comparing our method to the baselines across all metrics. Note that, for both InvertAvatar and GPAvatar, we use the official released code for comparison. For Avat3r, the code is not yet released but we thank the authors for providing their results on Ava-256 for us to compare to (unfortunately, their results on Nersemble data were not available).
The held-out data for the Ava-256 comparison includes 1000 different expressions from 12 subjects from different viewpoints. For Nersemble, we sampled 600 images from 4 subjects. We evaluate all methods at 512 x 512 pixels resolution, and for all results, comparison to Avat3r, which only used Ava-256 for training, we also train one version of our model on only the Ava-256 dataset (without Nersemble) and then show two separate results, one with only
four input images. For fair

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Table I: Quantitative comparison with state-of-the-art methods Ava-256 and Nersemble datasets.
Ava-256
NeRSemble
Method
PSNR 1
SSIM 1
LPIPS 4
CSIM 1
AKD 1
PSNR
SSIM 1
LPIPS
CSIM
AKD
InvertAvatar
14.2
0.36
0.55
0.29
15.8
15.1
0.47
0.46
0.45
8.2
GPAvatar
19.1
0.70
0.32
0.26
20.8
0.76
0.30
0.54
Avat3r
20.7
0.59
Ours (Ava-256)
22.2
0.76
0.24
0.71
4.8
22.3
0.78
0.27
0.70
4.8
Ours (both)
22.5
0.77
0.23
0.73
4.8
24.0
0.81
0.24
0.77
4.4
Ava-256 training data and one with both Ava-256 and Nersemble training data (labelled "both" the table and figures) to compare against the other methods.
As shown the table, our FastGHA method outperforms the baseline state-of-the seff-the-an art approaches with a large gap in reconstruction quality (PSNR, SSIM, LPIPS) and identity preservation (CSIM), with equal or better performance on facial keypoint similarity (AKD). With both Ava-256 and Nersemble datasets in training. our method performs better than when training Ava-256 alone, demonstrating that we can improve gencralization by training on more data.
Qualitative Comparisons. We also show visual comparisons of held-out subject reconstructions using FastGHA and the baseline methods. Three subjects from the Ava-256 test set are shown in Figure 3. and three from Nersemble shown in Figure 4 Note, again, that do not have Avat3r results for the Nersemble test set.
This qualitative comparison shows that InvertAvatar and GPAvatar unable to produce high fidelity results. Among the previous methods, only Avat3r can recover plausible photoreal avatars; however, it struggles with the identity recovery and does match the target expression well method. Important details like the hairstyle, skin texture and jewelry (earrings and nose rings) are better recovered by our FastGHA.
Input Images
InvertAvatar
GPAvatar
AvaL3r
(5va-256)
(both)
Figure 3: Qualitative reconstruction comparison on Ava-256 held-out subjects.
Analysis on the Number of Input Views. Although our method trained and mostly evaluated with four input views, our architecture supports any number of input images. Figure 5 illustratee the reconstruction quality for a variety of input views, from two six. Note how the overall reconstruction of the avatar agnostic to the exact number of inputs, and only fine-scale details are missing when we have only sparser views. In particular, using the first two input images only, the subject's 's left-side of the face is unseen, and details are hypothesized by method. Already by adding the third input view, left-side details are recovered.
We also measured the reconstruction time and real-time animation speed given the different number of inputs, as shown in Table 2 Because we infer per-pixel Gaussian attributes, the number Gaussians and thus the reconstruction time both increase rather linearly with the number of input views, and the
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Input Images
InvertAvatar
GPAvatar
Ours (Ava-256)
Ours (hoth)
GT
Figure 4: Qualitative reconstruction comparison on Nersemble held-out subjects.
Input Images
(train)
Figure 5: Analysis on the different number of input views. Two inputs means the first two in the top row, three inputs means the first three, four inputs adds the first image in the bottom row, and so on.
animation speed decreases accordingly, although still within real-time frame-rates even for six input images.
Analysis of Input Expressions. Since model is trained with arbitrary input views and expressions, we analyze the impact of different input expressions on the reconstructed canonical Gaussian and animated Gaussian head in Figure 6 There are slight differences in the animated results caused by the variation in input expressions. Nevertheless, the model consistently produces accurate expressions and stable animations even though our method is trained end-to-end without explicit supervision on the canonical Gaussian, we observe that our model is able disentangle identity and expression, by regressing the Gaussian avatar in a learned canonical representation. Specifically, when the four input images have different expressions, the model tends learn a "mean" canonical Gaussian that averages the variations. While when all input views share similar expressions, the canonical Gaussian would preserve that expression to some extent but tends be close neutral face.
when the four input views have significantly different expressions. Moreover,
4.3 ABLATION STUDY
We perform quantitative and qualitative ablations on several of our design decisions. The ablations are performed on the Ava-256 dataset. Note that the test set used for the ablation study differs from the one used in the main results, where the input images all share the same expressions as in Avat3r. The ablations are conducted on a separate test set where input images are with different expressions, which aligns with training paradigm. Quantitative results are shown in Table 3 with qualitative visual comparisons given Figure and the supplemental video. Specifically, we evaluate the following alternative strategies for network and training loss:
w/o VAE weights. In this version we do not use a pre-trained SD-Turbo VAE for our (frozen) encoder and (fine-tuned) decoder, but instead train the encoder and decoder from scratch using the same architecture. This experiment inspired by Avat3r, who train their own encoder/decoder. Superior rendering quality is achieved by including the pre-trained weights do in method.
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Table 2: Running time with different number of input images of method. All results exclude the time for obtaining camera and expression parameters that can be calculated in advance.
Number of Input
3
4
5
6
Reconstruction Time (s)
0.68
0.83
0.98
1.15
1.40
Animation Speed (FPS)
128
83
62
48
32
Inpur Images
Reconstructed Canonical Avatar
Animated Head Avatar
Figure 6: Visualization of the canonical and animated Gaussian head with different input expressions.
w/o ggen- Here show that without Lyeo based VGGT in training loss function, have worse 3D consistency and artifacts in the reconstruction.
w/o per-Gaussian features. Removing the learned per-Gaussian features from our network largely affects the animation accuracy (please refer to the supplemental video).
Sapiens features. We also trained version of network using Sapiens features (Khirodkar et al. 2024 in place of DINOv3, following Avat3r. Using the DINOv3 features provides better results.
Input Images w/o VAE weights
w/o 5
w/o Gauss, feats.
Sapiens features
Ours
GT
Figure 7: Ablation study showing the importance each of our design decisions.
4.4 APPLICATIONS
Extreme Poses and Expressions. We first showcase the capability of our method by animating and rendering the head avatar in extreme expressions and large view angles in Figure 8 In particular, we render novel views reaching 90%, even though such poses lie far outside the distribution of the input images.
In-the-wild Results. We end demonstrating our method on the practical application of reconstructing 3D Gaussian avatars from candid photos in-the-wild. Different examples are shown in Figure Our model supports portrait images captured from phone scans, monocular video frames, and images generated from multi view diffusion model. Even though the input images outside the distribution of lighting and camera angles from the it-sto training datasets, our method has learned generalize and creates realistic avatars that be animated and rendered from novel views.

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Table 3: Ablation results the Ava-256 dataset.
Method
PSNR
SSIM
IPIPS +
CSIM 1
AKD +
w/o VAE weights
20.789
0.738
0.251
0.681
5.487
w/o Egeo
21.132
0.741
0.239
0.687
5,049
w/o per-Gaussian features
21.053
0.740
0.245
0.690
5.216
Sapiens features
21.081
0.741
0.241
0.689
5,021
Ours
21.274
0.745
0.237
0.704
4.996
Input Images
Animated Head Avatar
Figure 8: Animation under large poses and expressions.
DISCUSSION
In this work we present FastGHA, a new feed-forward method to build a high-quality Gaussian avatar from sparse number of input views. Our experiments have shown that novel network design improves over both quality and efficiency compared to current state-of-the-ard methods. In particular, FastGHA allows avatar reconstruction in under one second, followed by real-time animation and rendering, and can be applied to in-the-wild images.
Limitations. Our method shares similar limitations Avat3r, in that rely accurate camera parameters as input, and our reconstructed avatars retain the same shading as the input images. Additionally, for our expression code parameterization we chose the FLAME expression space, offers intuitive artistic control over the resulting avatars. Unfortunately it does not allow to control the tongue motion. Other expression parameterizations like the one from Ava-256 could be used, but are less easily controllable for novel animation.
Ethical Considerations. Given only a small number of images, our method allows users generate synthetic animations of person. This technology should only be used with the consent of the subject. We do not support any unethical use of our method.
REPRODUCIBILITY STATEMENT
We strive provide all the details required to reproduce our approach, including the architec- ture overview (Section 3.I), loss function details (Section 3.2), ablations on altemate variations (Section 4.3), and fine-level architecture details (in supplemental document).
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A APPENDIX
In this supplementary material, we provide more details about the network architecture, data process- ing, and more information on our training details. We recommend watching the supplementary video for even more results.
A.1 NETWORK ARCHITECTURE
We describe more implementation details on the network architecture used in Section 4.1 of the main paper. In Table 4| present the details of our network architecture and the hyperparameters setting for our HastGHA. We freeze the DINOv3 and Diffusion VAE encoder for feature extraction and faster learning. For the VAE decoder, extend the input layer dimension 512 match the output token dimension of the Multi-view Transformer. We also change the middle attention layers to cross-view attention for better view consistency. The VAE decoder is meant to regress pixel-wise Gaussian attributes, which includes position X, multi-channel color C, rotation Q, scale S, opacity a, and per- Gaussian feature f, in total 44 dimensions. Thus, we also expand the output layer dimension to predict all these attributes for each pixel. Similar to previous work Giebenhain et al. (2024); Dhamo al.) (2024), we leverage an MLP predict Gaussian deformations from an expression code. To capture fine details of the motion, we apply sinusoidal positional encoding on the Gaussian position X.
A.2 TRAINING DETAILS
Dataset processing, We use large multi-view video dataset Ava-256 and Nersemble for training our model. In Ava-256 dataset, we leave 30 identities for testing, while in Nersemble dataset, we use 300 subjects for training. To factor the head pose inside the camera parameters, we use the head poses provided from the tracked mesh for the Ava-256 dataset. For the Nersemble dataset, we photometric head tracker based VHAP QQian et al. 2024) to fit FLAME parameters with multi-view images and known camera parameters. Then, we infer the head pose transformation from the FLAME parameters that the network does not have to predict them.
Input settings. We train our network with four view images as input. Similar to Avat3r (Kirschstein et al. 2025). we leverage k-farthest viewpoint sampling during training for both Ava-256 and Nersemble dataset to ensure a reasonable distribution of these images. Moreover, we take input images from different timesteps to improve the robustness of the model. Specifically, we first sample random camera and then use the farthest point sampling to collect the remaining 7 cameras for training and supervision. Then, for the input, we randomly select timesteps inside the same sentence and one timestep for supervision.
VGGT Geometry Loss. To enhance the 3D geometry consistency and accelerate the training, we leverage the 3D prior from large reconstruction model VGGT as supervision during training. VGGT able to reconstruct 3D scene from arbitrary number of inputs with predicted camera parameters, depth map, point map etc. However, we found out that the quality of the reconstructed camera and point cloud is not high enough and inconsistency exists. Thus, we take the 3d prior as a restrictive loss instead of an input for skip connection in Avat3r. Since VGGT is large foundation model, pre-compute the point maps for all camera views of each timestep. To get the point cloud that are aligned with the dataset camera, the tracked mesh. Specifically, we choose one frontal view and learn a transformation between the VGGT keypoint and tracked mesh keypoints for given facial landmarks. Then, we could transform and use the transformed point map as geometry supervision during training. Note that here we use the depth map calculate the loss since we have the ground-truth camera parameters.
the 2d landmark along with the corresponding 3d keypoint in
A.3 ADDITIONAL RESULTS
A.3.1 MORE ABLATION RESULTS
We analyze the contribution of DINOv3 feature map, and the efficacy of per- Ger-Gaussian feature dimension here. The quantitative results are listed in Table 5) and we show qualitative comparisons in Figure 9 We observe noticeable degradation of shape consistency when the DINOv3 feature

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Table 4: Hyperparameters.
Hyperparameter
Value
Input image resolution
512 x 512
Input & Output
Gaussian attribute map resolution
512 512
Train render resolution
512 512
DINOv3 version
dinov3-vit116
DINOv3 feature size
V x 256 x 64 x 64
Feature Extraction
Diffusion VAE version
SD-Turbo
VAE encoder feature size
V 4 64 64
Camera ray size
x 6 x 64 64
ViT patch size
8 x 8
Hidden dimension
512
Multi-view Transformer
Self-attention layers
Input token size
266 x V x 64 x 64
Output token size
512
x x 64
Dimension of expression code
120
Dimension of per- GausGann feature
32
Deformation MLP
Hidden dimension
256
Number of hidden layers
MLP activation
LeakyReLU
Table 5: More ablation results on the Ava-256 dataset.
Method
PSNR t
SSIM t
LPIPS
CSIM
AKD 4
w/o DINOv3
21.068
0.748
0.255
0.651
5.329
per- per-Gaussian 16
21.220
0.739
0.251
0.672
5,190
Ger-Gausiian feature-64
21.420
0.755
0.243
0.697
4,933
Ours (DINOv3 + fcature-32)
21.274
0.745
0.237
0.704
4.996
map is removed. Morcover, we analyze the performance with different choices the per-Gaussian feature dimension. Results show that higher dimension of per-Gaussian feature only brings marginal improvement, while decreasing the dimension noticeably degrades the animation accuracy.
A.3.2 IN-THE- WILD RESULTS
Although our model is trained with laboratory datasets with ground-truth calibrated cameras, generalizes well to wnlthe-wild input images with unknown camera parameters. In FFigure I we show several use-cases of FastGHA, including portrait images captured from phone scans, monocular video frames, and images generated from multi-view diffusion model (Taubner et al.) 2025). Specifically, for images lacking camera calibration, we use an off-the-shelf monocular FLAME tracker (Zielonkal al. 2022 Giebenhain al.) 2025) to estimate the camera parameters with face meshes under the same canonical space. While the estimated cameras inevitably contain errors, our model still produces high-quality animations, indicating that our model is robust moderate perturbation of cameras and generalizes well to unseen subjects, lighting conditions, and even cartoon characters.
A.3.3 COMPARISON WITH ONE-SHOT METHODS
To evaluate the performance of our model with a single input image, we show comparisons with state- of-the-art 3D aware one-shot methods, including GAGAvatar (Chu & Harada 2024) and LAM (He
2

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Input Images
w/o DINOv3 Gauss, fcats.-16
Gauss. fcats.
Ours
GT
Figure More qualitative ablation of our design choices.
Table 6: Quantitative comparison with one-shot methods on the Nersemble dataset.
Method
PSNR
SSIM
LPIPS
CSIM
AKD +
GAGAvatar
20.639
0.760
0.296
0.657
5.262
LAM
20.008
0.756
0.288
0.557
6.328
Ours (one)
19.299
0.729
0.327
0.497
8.249
Ours (one+fiictuned)
23.002
0.790
0.263
0.730
4.725
et al. 2025). Both GAGAvatar and LAM rely on a 3D morphable model to reconstruct and animate the Gaussian avatar from single image. Moreover, GAGAvatar performs pixel-level post-processing with 2D neural network, so it is not fully 3D. Quantitative and qualitative results are shown in Table and Figure reeppectivll Here, for each image input. For GAGAvatar, we use their official FLAME tracker for data processing. For LAM, we use the same processed data as our model.
subject the Nersemble dataset, we select one frontal
Since our model is trained using four input images, directly applying it to single input image results holes in face regions, especially the unseen part. For a fair comparison, we fine-tune our model under the one-shot scenario for another 30k steps, which takes about 6 hours and denote it as "Ours (fine-tuned)". As can be seen in both the quantitative and qualitative results, our method achieves better identity preservation and expression accuracy, and the fine-tuned model can also hallucinate unseen areas in the single image input.
Input Image
GAGAvatar
LAM
Ours (one)
Ours (fine-tuned)
GT
Figure 10: Qualitative comparison with sho- methods.
A.4 ANALYSIS MODEL DESIGN AND PERFORMANCE
A.4.1 ANALYSIS OF GAUSSIAN OFFSETS
As mentioned in the main paper, predict the offsets of both per-Gaussian position and color from an MLP to model the facial dynamics. Here, we further evaluate the effectiveness of learning color offsets by comparing animation results with and without color offsets prediction. As be in infFiguee II] learning the color offsets is important for modeling fine- mne-grained appearance, including wrinkles, skin details, and the teeth region when animating the avatar.

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Inpet Images
color
w/o
Ours
Figure 11: Effect of learning color offsets for each Gaussian.
A.4.2 ANALYSIS OF NUMBER OF INPUT VIEWS
In Figure 12
we show how the performance of our model scales with the number of input views on both the Ava-256 and the Nersemble dataset. The results show clear performance improvement as more input views are provided. However, more input views leads to higher computation cost and longer runtime inside the cross-attention network and the rendering of more number of Gaussian primitives. Moreover, the performance gain of using more than four input views becomes marginal.
Ava-256
Narsemble
25
23.6
22.2
PSNR
Ixput View Numbers
Achieved reconstruction quality with different number of input views, averaged over all subjects in the respective dataset.
Figure 12:
A.4.3 MORE ANALYSIS OF INPUT EXPRESSIONS
Here, we show more visualizations of the reconstructed canonical head and animated Gaussian head in Figure 13 The canonical Gaussian head is fully learned by the model without supervision, and deformation MLP is trained predict offsets relative to this space. We identify two factors that make the network converge to relatively neutral canonical Gaussian head even when the input images contain expressions. First the dataset distribution. Most of training frames depict neutral or near-neutral expressions. As result, the model is encouraged to predict canonical Gaussian representation that minimizes deformation inside the dataset. Second is our expression -driven deformation design. We use the FLAME expression paramater as driving signal for the deformation network to predict Gaussian offsets. Under such circumstances, a neutral-like canonical shape could provide stable reference for animation driven by the FLAME expression parameters.
A.4.4 ANALYSIS ON CAMERA DEGRADATION
Here evaluate the robustness of our model to camera perturbations by gradually adding noise to the ground-truth camera poses. As shown in Figure 14 inaccurate camera inputs noticeably degrade the multi-view consistency during animation. However, of camera perturbations, which is further supported by the in-the-wild examples in in/iigue I where camera parameters obtained through monocular optimization. Since model is trained datasets with accurate calibrated cameras, one potential direction to improve the robustness of the model is to add camera noise augmentation during training or alternatively developing an architecture that requires no explicit dependence on camera parameters. We leave this for future work.
model remains stable for small levels
4

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Input Images
Reconstmeted Canonical Avaiar
Animated Head Avatar
Figure 13: More visualization of the canonical and animated Gaussian head with different input expressions.
Inpul Images
Figure 14: Impact of noise in the input camera poses on the quality of the generated avatar.
5


Reviews:
[{"review_id": "ePEGC19Nzn", "reviewer": "Reviewer_2Epj", "summary": "This paper proposes FastGHA, which efficiently generates high-quality, real-time animated 3D Gaussian head avatars with few-shot inputs. The method fuses multi-view features through a Transformer encoder and utilizes Gaussian features and a lightweight MLP network to achieve real-time animation based on facial expression encoding. Simultaneously, geometric supervision is introduced to improve the geometric smoothness of the 3D head.", "questions": "* Why does the MLP need to modify the color of the Gaussians during animation? What would happen if it didn’t?\n* The paper conducts ablation experiments on removing the VAE feature. What would be the result if only the VAE feature is kept and the DINO v3/Sapiens feature is removed?\n* Removing the per-Gaussian feature shows a significant difference in results. What would happen with other dimension choice of the per-Gaussian feature, for example, 16 or 64?\n* This method seems to have a hidden limitation: the requirement for a reasonable distribution of input viewpoints. Does this mean that the method lacks or has limited reconstruction ability when certain viewpoints are not covered? Structurally, the method can reduce the input to just one image. What is the performance when only a single frontal image is provided, or what if one frontal image and one side image, with the other side / up side missing?\n* As a few-shot method, it is also reasonable and beneficial to discuss and compare with some of the latest one-shot methods, which can highlight the unique advantages of few-shot approaches. The authors could discuss and compare with methods such as GAGAvatar and LAM.\n* Since the method is completely unsupervised in the canonical space, the statement “By design, the canonical head\nis devoid of expression and thus does not correspond to any of the input expressions, allowing animation to happen downstream. ” is too arbitrary. It is possible for the model to learn a canonical space with an open mouth, and in fact, a canonical space with an open mouth may make it easier to model teeth than the closed-mouth shown in the Figure 1. What causes the model converges to a closed-mouth canonical space?\n* There is no explanation for C_d and C_e in Equation 3. After reading, it seems refer to the DINO feature and encoder feature, but this should be explicitly stated.\n* The additional Gaussian features used in animation MLP should not be claimed as \"learnable\", because it is not parameters of the model, but rather the result of inference. They are essentially the same thing as other inferred Gaussian parameters.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The method demonstrates satisfactory few-shot reconstruction quality and visualization effects.\n* The method's reconstruction design is reasonable, effectively integrating information from different views, and can be well driven by new expressions.", "weaknesses": "* This method may struggle with \"unreasonable\" user input and the paper does not show these results. Furthermore, reasonable input is difficult to define. For example, if user input from a particular perspective is missing, the result is currently unknown. \n* This method may struggle to handle an arbitrary number of input viewpoints. Because it relies on VxHxW self-attention, too many views exponentially increase the computational cost of this part.\n* Due to the linear increase in the number of Gaussians and the presence of the driving MLP, the driving speed decreases linearly with increasing viewpoint. Merging and controlling the total number of reconstructed Gaussians might be a good solution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c4P92TtYXW", "reviewer": "Reviewer_uEfC", "summary": "The paper addresses the task of reconstructing an animatable 3DGS-based avatar from 4 posed input images.\n\nFollowing the recent success of LRM-style approached, like Avat3r,, the method employs a multi-view transformer which use features from a stable diffusion VAE, DINOv2 and plucker ray embeddings to predict per-pixel Gaussian attributes. \n\nImportantly, the set of gaussian attributes is extended to contain per-Gaussian latent features, similar to GHA and NPGA, which are then used to condition an MLP which animates the Gaussian based on FLAME expression code conditoning. Compared to Avat3r, the MLP is more light weight than the cross-attention based architecture, which is relevant for real-time applications.\n\nAdding supervision against aligned VGGT depth predictions helps to produce more 3D consistent avatars.\n\nThe method is thoroughly evaluated against comparable sota baselines, and additionally shows examples on real-world images.", "questions": "- The text states that the 4 input views have different facial expressions during training. However, all results show identitcal expressions, or quite similar expressions. Does the model still perform well with very different expressions?\n- Fig. 5: would be interesting to see how much the canonical point cloud changes with different inputs, e.g. the first example has the mouth slightly open in canonical space, which might be caused by the input images.\n-  Is the VGGT superivsion competed offline? How good do the recoinstructions look? In my experience they can be rather blurry for faces and exhibit many stiching artifacts. Could this have been done with e.g. COLMAP as well?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The task of accessable avatar creation from a few selfie images finds importance in many down-stream applications. Therefore, the improved visual quality and faster animation speed immediatley become more significant. \n- Smart and simple usege of pretrained VAE weights for encoder/decoder, which is also ablated to be beneficial. The same can be said for the VGGT supervision.\n- Overall the architecture seems to be slightly simplified comapred to Avat3r, which seems quite helful for future improvements that build upon the presented method.\n- The paper is well-written and easy to follow.\n- The method is thoroughly evaluated against to most relevant baselines, and on two different datasets, leaving little room for doubt about the evaluation. Furthermore, the SOTA baselines are significantly outperformed.\n-", "weaknesses": "- The main weakness of the paper that I am still seeing is the limited novelty, since it mainly introduces some technical changes compares to Avat3r. However, the quality and animation speed are improved, and the paper is well evaluted. Therefore, we can be sure that the method solidly advances the field on such a highly relevant task. Therefore, I don't mind the limited novelty.\n- Currently, the method is limited by FLAME expression codes. However, anything else would likely be out-of-scope for this project, and it would make animation from 2D videos arguably harder, bc. obtaining FLAME codes is so easy.\n- Somewhow the number in the ablation study don't match the main results. Is there an explanation for this? Otherwise, this gives the impression that the ablations were sloppily exectued.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "k2FXHCRzlv", "reviewer": "Reviewer_XoYB", "summary": "The paper introduces FastGHA, a novel feed-forward method designed to overcome the challenges of efficiently generating high-fidelity 3D Gaussian-based head avatars by utilizing only a few input images.\n\nThis approach notably supports real-time animation and achieves high fidelity through a two-stage pipeline: first reconstructing a canonical Gaussian head representation, which disentangles identity from the input expressions, followed by a deformation process. The system learns a per-pixel Gaussian representation, aggregating multi-view information using a transformer-based encoder that fuses semantic features from DINOv3 and color features from a Stable Diffusion VAE. For animation, the explicit Gaussian representation is augmented with learnable per-Gaussian features, and a lightweight MLP-based dynamic network is introduced to predict fine-scale 3D Gaussian deformations using descriptive expression codes, such as FLAME parameters. To improve the geometric consistency and robustness of the resulting 3D head, the method employs point maps derived from a pre-trained large reconstruction model (VGGT) as geometry supervision in the form of a regularization loss during training.\n\nExtensive experiments confirm that FastGHA significantly outperforms existing state-of-the-art methods in both rendering quality and inference efficiency, enabling avatar reconstruction in less than one second.", "questions": "- In the qualitative comparisons (as shown in video), I still observe flicker in the Gaussian primitives. Could the authors explain the cause of these artifacts? Would increasing the number of input views alleviate the issue? Including curves of performance versus number of views in the final version would help analyze the impact of few-shot inputs.\n\n- The method reconstructs a canonical Gaussian Head even when the input images contain expressions, and the authors state that the canonical Gaussian is unsupervised. I am curious why this representation can be learned. Could the authors offer a deeper discussion? Is this related to using neutral faces during training, enabling the model to learn that, when the expression code is absent, it should output a canonical Gaussian?\n\n- Could the authors discuss whether the pre-processing of VGGT—specifically the alignment of its reconstructed point cloud using 2D landmarks and corresponding 3D keypoints from a tracked mesh—introduces additional errors or complexities? In particular, how do inaccuracies in landmark detection and mesh tracking affect the reliability of the geometry prior and the regularization loss $L_{geo}$, given that the VGGT point cloud quality is reported as \"not high enough\" and \"inconsistent\" (Line 849-850)?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The paper presents a clear methodology and is well-structured.\n- FastGHA employs a feed-forward approach to directly predict a per-pixel Gaussian representation. This design is explicitly chosen to enable instantaneous reconstruction of unseen subjects, avoiding lengthy per-identity optimization or template Gaussians rigged to 3DMMs required by many prior methods. Previous approaches struggled to handle fluffy elements like hair due to the rigging constraint.\n- The method significantly addresses efficiency drawbacks by supporting real-time dynamic avatar animation. This speed is achieved through a lightweight MLP-based dynamic network that predicts fine-scale Gaussian deformations from expression codes, contrasting with computationally slower methods like those using cross-attention blocks. For a standard four-image input, the avatar reconstruction takes less than one second, and the animation speed remains high (e.g., 62 FPS for four inputs).\n- FastGHA demonstrably outperforms existing state-of-the-art methods (including Avat3r, InvertAvatar, and GPAvatar) in reconstruction fidelity, showing a large gap in quantitative metrics such as PSNR, SSIM, LPIPS, and identity preservation.", "weaknesses": "- The paper identifies Avat3r as the \"most related\" state-of-the-art method. However, the crucial quantitative comparison table (Table 1) lacks the results for Avat3r on the Nersemble dataset. This prevents a full comparison, especially given that FastGHA's best performance is achieved when training on both Ava-256 and Nersemble (\"Ours (both)\").\n- The animation pipeline depends on the accuracy of FLAME expression codes ($z_{exp}$) obtained using \"off-the-shelf head tracking tools\". Errors generated by these trackers when processing complex or extreme expressions (e.g., a wide open mouth or squinting, as shown in the input examples) could compromise the fidelity of the downstream deformation network $D$.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SuiHnTXEiu", "reviewer": "Reviewer_4bsZ", "summary": "This paper introduces a feed-forward framework for reconstructing animatable 3D Gaussian head avatars from only a few input images. The model first reconstructs a canonical Gaussian head using multi-view features extracted from pre-trained DINOv3 and Stable Diffusion VAE encoders, fused by a transformer-based module. Then, a lightweight MLP conditioned on FLAME expression codes predicts per-Gaussian deformations for real-time facial animation. Additionally, a geometry prior from the VGGT is employed as regularization to improve 3D consistency.", "questions": "- How does the model behave if camera calibration is slightly inaccurate or unavailable? Could the method be extended to work with monocular or unposed multi-view inputs?\n\n- Can diffusion-based priors help improve in-the-wild generalization?\n\n- Please provide more quantitative metrics for novel-view (side/back-view) and novel-expression performance.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear two-stage design that separates canonical reconstruction and dynamic animation.\n\n- Efficient inference: <1s reconstruction and >60 FPS animation.\n\n- Use of VGGT geometry prior enhances structural consistency.\n\n- Outperforms Avat3r quantitatively in controlled datasets.\n\n- Technically elegant combination of DINOv3, diffusion VAE, and Gaussian Splatting.", "weaknesses": "-  Methodological innovation over Avat3r is relatively incremental, primarily combining known modules (DINOv3, VAE, VGGT) rather than introducing a fundamentally new approach.\n- Strong dependency on accurate camera poses, which significantly limits real-world usability. The method cannot operate from uncalibrated or monocular video, unlike many recent “in-the-wild” approaches, even though the paper acknowledges it in the limitations.\n\n- In-the-wild results are weak and underexplored. Only a few examples are shown, and they exhibit degraded appearance and noticeable artifacts. The paper should provide more analysis on why generalization drops and how to mitigate it.\n\n- Mouth region artifacts are frequent (see Figs. 2–3 and video results), with incomplete geometry and texture distortions, suggesting the method struggles with open-mouth or speaking expressions.\n\n- Most visualizations show static reconstructions, not novel-view or novel-expression results, which are more important in avatar applications.\n\n- The paper lacks convincing results for large-viewpoint novel renderings; side-view artifacts are visible in the supplementary video, contradicting the claim of a fully 3D avatar.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)