FastAvatar: Towards Unified and Fast 3D Avatar Reconstruction with Large Gaussian Reconstruction Transformers
Authors: Yue Wu
Xuanhong Chen
Yufan Wu
Wen Li
Yuxi Lu
Kairui Feng
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Avatar
3D Reconstruction
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Despite significant progress in 3D avatar reconstruction, it still faces challenges such as high time complexity, sensitivity to data quality, and low data utilization. We propose~\textbf{FastAvatar}, a feedforward 3D avatar framework capable of flexibly leveraging diverse daily recordings (e.g., a single image, multi-view observations, or monocular video) to reconstruct a high-quality 3D Gaussian Splatting (3DGS) model within seconds, using only a single unified model. The core of FastAvatar is a Large Gaussian Reconstruction Transformer (LGRT) featuring three key designs: First, a 3DGS transformer aggregating multi-frame cues while injecting initial 3D prompt to predict the corresponding registered canonical 3DGS representations; Second, multi-granular guidance encoding (camera pose, expression coefficient, head pose) mitigating animation-induced misalignment for variable-length inputs;
Third, incremental Gaussian aggregation via landmark tracking and sliced fusion losses. Integrating these features, FastAvatar enables incremental reconstruction, i.e., improving quality with more observations without wasting input data as in previous works.  This yields a quality-speed-tunable paradigm for highly usable 3D avatar modeling. Extensive experiments show that FastAvatar has a higher quality and highly competitive speed compared to existing methods.

Published as a conference paper at ICLR 2026
FASTAVATAR: TOWARDS UNIFIED AND FAST 3D AVATAR RECONSTRUCTION WITH LARGE GAUSSIAN RECONSTRUCTION TRANSFORMERS
Yue Wut.2 Xuanhong Chen Yufan Wu* Wen Li
Yuxi Lu
Kairui Feng-. 1.2
Tongji University! Shanghai Innovation Institute Shanghai Jiao Tong University AKool
yuewuêtengji. edu. en, chenxuanhongee en,
xeLvintkr@tonnjj..com
Monocular Images or Shots
Multi- View Shots
Modeling
Modeling
Unifled
FastAvatar
Frone
Frame
Frame
Frame
Reenactment
Figure 1: Unlike existing 3D Avatar methods that only process fixed-length data, FastAvatar achieves incremental reconstruction. can strike a good balance between modeling quality and inference speed based available data volume, delivering high-quality models with sufficient data while providing viable reconstruction results at high speed even with limited data.
ABSTRACT
Despite significant progress in 3D avatar reconstruction, it still faces challenges such high time complexity, sensitivity to data quality, and low data utiliza- tion. We propose FastAvatar, a feedforward 3D avatar framework capable of flexibly leveraging diverse daily recordings (e.g., single image, multi-view ob- servations, or monocular video) to reconstruct high-quality 3D Gaussian Splat- ting (3DGS) model within seconds, using only a single unified model. The core of FastAvatar is Large Gaussian Reconstruction Transformer (LGRT) featur- ing three key designs: First, a 3DGS transformer aggregating multi-frame cues while injecting initial 3D prompt predict the corresponding registered canon- ical 3DGS representations; Second, multi-granular guidance encoding (camera pose, expression coefficient, head pose) mitigating animation-induced misalign- ment for variable-length inputs; Third, incremental Gaussian aggregation via landmark tracking and sliced fusion losses. Integrating these features, Fas- tAvatar enables incremental reconstruction, i.e., improving quality with more observations without wasting input data as previous works. This yields a suality-speed-tunabbe paradigm for highly usable 3D avatar modeling. Exten- sive experiments show that FastAvatar has a higher quality and highly compet- itive speed compared to existing methods. Code and models available at https::/gitte com/ www.taabb.cco/yyyyyyoooa////aaaa
INTRODUCTION
Creating photorealistic 3D avatar reconstruction one of the fundamental problems in computer
vision and graphics. Contemporary methods Kirschstein et al. (2025): He et al. (2025): Chen et al.
"Corresponding authors

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(024c): Qian et al. (2024a); Pan et al. (2024); Wen et al. (2024); Qian et al. (2024b); Jiang et al. 2024): Hu et al. (222)); ; Qiu et al. (2025) for 3D avatars have made significant advancements in 3D representation and modeling quality. However, these approaches commonly suffer from drawbacks such as data sensitivity (e.g. requiring richly expressive data), high time complexity, and low data utilization efficiency. These issues, pose severe challenges to the low-cost application of 3D avatars.
Three factors hinder existing 3D avatar methods from addressing the aforementioned challenges: 1) Inability to Leverage Prior Knowledge: Although contemporary 3D avatars have widely adopted efficient representations like 3DGS Kerbl et al. (2023), they still primarily rely on per-scene op- timization. This approach fails to utilize "experience" from similar scenes, preventing the acqui- sition of good initial values to accelerate optimization. Furthermore, since all model information stems solely from the input observations, missing data cannot be reconstructed, resulting a heavy dependence on complete 3D observations. Daily captures, however, often contain significant in- formation gaps. 2) Low Accuracy in Observation Alignment: 3D avatar methods typically depend entirely on parametric proxy models (c.g.. 3DMM/FLAME Blanz& & Vetter (1999); Li et al. (2017)) for coarse view alignment. The precision of this alignment is critical for effective modeling; For instance, GaussianAvatars Qian et al. (2024a) even requires the proxy model to provide detailed meshes for hair. However, these parametric models are susceptible to limitations in representational capacity (e.g., blendshapes from 31) scan databases). lighting conditions, and data quality, often failing to produce highly accurate proxy 3D models. Using this proxy without refinement leads to poor robustness, hindering unified adaptation diverse data sources (e.g., light fields, smartphones, DSLR cameras). 3) Inadequate Handling of Sariable-Lengte Data: Optimization- based 3D avatar methods typically require input data of minimum specific length (typically at least 30 seconds at 25fps). Insufficient data often leads to modeling failure, resulting in severely limited capability to process few-shot data (e.g., frame, frames). Meanwhile, recently proposed feedforward-ste -tyyle methods Kirschstein et al. ) He et al. (2025) are usually designed for fixed-length inputs for training convenience. For instance, LAM He al. (2025) can only process single-frame input, and Avat3r Kirschstein al. (0255 is fixed to handling exactly 4 frames. However, woal-world data can consist of any arbitrary number of frames. The inability to process inputs of variable lengths will result in wasted valuable observation data, consequently limiting modeling quality.
To pursue data-efficient, high-quality, and fast 3D avatar reconstruction, we propose FastAvatar. It enables direct feedforward reconstruction of animatable avatars within seconds from ceiitrary-lengtee input frames and can incrementally leverage additional observation data. The core of FastAvatar is Large Gaussian Reconstruction Transformer (LGRT). It can align and aggregate variable-length facial inputs based on head pose and camera pose, then generate high-quality Gaussian model groups using coarse 3D positional prompts. Finally, these groups can be flexibly fused into a single 3DGS avatar model according to quality requirements and computational resources. Notably, compared to
LAM(He et al. 2025) and Avat3r Kirschsinnn et al. (2025). FastAvatar handles variable-length obser- vation data with greater model flexibility and higher data utilization efficiency. Unlike VGGT Wang ct al. (2025a), FastAvatar is capable of directly generating 3DGS avatars and can achieve granular 3D model aggregation. Benefiting from the successful architecture of VGGT, LGRT is designed as a variant the VGGT structure. We replace the unstable Dense Prediction Transformer (DPT) Ran- ftll et al. (2021) with MLP that directly predicts canonical 3DGS models, while adopting 3D parametric model (c.g., FLAME) mesh vertices as positional prompts for the output. These im- provements maximize adaptability for the prediction of the 3DGS avatar. Instead of relying solely on single camera pose encoding, 3D avatar reconstruction demands higher requirements for input data alignment. Therefore, we additionally incorporate expression coefficients and head pose as positional encoding for input observations, enabling more precise tross-frame information aggrega- tion. Critically, we propose a landmark tracking loss and sliced fusion loss to efficiently supervise the model for enhanced aggregation accuracy while enabling incremental 3DGS models fusion. In- tegrating these key designs, our model pioneers incremental 3D avatar reconstruction, meaning can continuously ingest new observational data to progressively refine modeling quality.
Extensive experiments demonstrate that our model achieves highly competitive 3D reconstruction quality compared to state-of-the--e methods. It uniquely accomplishes incremental 3D avatar recon- struction, currently unattainable by existing approaches, and holds promise for delivering favorable solutions in the quality-speed trade-off paradigm.
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2 RELATED WORK
2.1 3D-BASED HEAD AVATAR RECONSTRUCTION
FLsde-based Li et al. 2017); Feng et al. (2021); Daneček et al. (2022); Ma et al. (2024); Cudeiro
et al. (2019) techniques utilize parametric model in head reconstruction, allowing for effective
expression control but struggle to represent details (e.g., eyes, teeth) and limited to single-view. Since neural radiance fields have demonstrated strong ability synthesis photo-realistic images, some method Zielonka et al. (2023): Shao et al. (2023); Athar et al.] 2023); Müller et al. (2022) have adopted NeRF Mildenhall et al. (2021) for head reconstruction, which perform higher fidelity, particularly in modeling fine-scale details like hair. However, NeRF-based approaches |Athar et al.
2022 Guo et al. (2021) :: Liu et al. (2022) often suffer from significant issue with head rendering speed limitations and extensive training data. Recently, 3DGS Kerbl et al. (2023) has demonstrated superior performance surpassing NeRF in both novel view synthesis quality and rendering speed. Approaches Qian et al. (2024a); Chen et al. (2024c); Xu et al. (2024); Wang et al. (2025b); Wu ct al. (025) generate photorealistic head avatars that allow full control over expressions and poses using multi-view videos. Another line of research places explicit emphasis identity preserva- tion Gerogiannis et al. (2025): ZZenng et al. (2025): Zielonka et al.] (2025). Despite 3DGS's ' im- pressive performance, requires multi-frame data for identity-specific training and lacks flexibility, necessitating separate models for single-view and multi-view scenarios. In contrast, our FastAvatar achieves ultra-fast 3D head avatar reconstruction with a unified model.
2.2 FEED- FORDAR RECONSTRUCTION MODEL
Traditionally, 3D reconstruction and view synthesis, depending on optimization- approaches such as Sonecture-froo-Moon Schonberger & Frahm (2016) and Multi-View Stereo Sccönbergerr al.] (2016), are often computationally intensive, slow to converge, and reliant on precisely cali- brated dataset, limiting their applications in real-world scenarios. Recently, series of research Wang jet al. (2024); Chen et al. (2024b); Liu et al. (2024); Ye et al. (2024); Hong et al. (2023); Charatan et al. (2024); Szymanowicz et al. 2024) Tang et al. (2024); Jin et al. (2024); Zhang al. (024a);;
Jiang et al. (2025) initiate a new research paradigm termed Ford-forward 3D reconstruction model. DUS(3R Wang et al. (2024) introduces method for dense and unconstrained stereo 3D reconstruc- tion, operating without prior camera calibration or viewpoint poses. VGGT Wang et al. (2025a) uses large feed-forward transformer effectively predict all key 3D attributes from single im- age or multiple images. While feed-forward networks excel generic 3D reconstruction, their application to 3D head avatar reconstruction is still nascent and warrants systematic exploration. LAM He et al. (2025) introduces feed-forward framework that reconstructs an animatable gaus- sian head from single image, allowing animation and rendering without additional post-processing. Avat3r Kirschstein et al. (2025) regresses animatable 3D head avatar from just few input images, reducing compute requirements during inference. A key challenge Feed-forward Head Recon- struction Model is the absence of unified framework to handle diverse real-world inputs, includ- ing monocular videos, sparse multi-view captures. To address this gap, we propose a VGGT-style transformer architecture to jointly process different observation resource, achieving state-of-the-art performance.
3 METHODOLOGY
Daily observations are diverse and varied, such as single selfies, multi-angle selfies, video vlogs, etc. In summary, they are variable-length. Existing optimization-bant based 3D Avatar methods Kirschstein ct al. (2025); Chen et al.] (2024c); Qian et al. (2024a) cannot effectively handle overly short data (typically a single image). FastAvatar was specifically designed to address this scenario.
3.1 PROBLEM DEFINITION AND NOTATION
FastAvatar G(.) is a feed-forward avatar reconstruction framework designed to take any number of input observations and output a high-quality animatable 3DGS avatar:
A <- G(T, n, Reaps Zpose);
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Asgragated Tokens
Canonical 3DG5
Expression
L
Rasterization
View Toben
Frame
Attention
GS Head
9
ig
DIND
LBS
GT
View
Global Attention
Camera Dim
Lint Load
Framé Attention
GS Head
um Hosa
DINO
View
View
View N Taken
Frama Attention
GS Head
Siivel
DING
View It
GT
Fusion
Figure 2: The core FastAvatar a Large Gaussian Reconstruction Transformer (LGRT), which flexibly process input data with varying expressions, poses, and camera angles, aggregating them into high-precision 3DGS avatar model. This capability is enabled by several key designs: the interleaving of global attention and frame attention register complex input data while encod- ing 3D positional prompts; multi-granular positional information encoding, and the use of landmark tracking loss and sliced fusion loss, allowing the model smoothly and incrementally fuse addi- tional input data.
where (4)) denotes an unordered sequence of N RGB observations, with each 1, R3xHXW N does not exceed the maximum GPU capacity, typically 16 frames. The corresponding facial expression and head pose are represented by Zeep and 2pose: respectively. π denotes the camera parameters and poses. The output 3D Gaussian avatar representation 9, including color E RR opacity I. per-axis scale factors R, rotation offest O E R can be driven by any desired expression and pose from an arbitrary viewpoint using differentiable rasterizer V Kerbl et (2233)
R4, importance score E and points
3.2 LARGE GAUSSIAN RECONSTRUCTION TRANSFORMER
The core of FastAvatar is a Large Gaussian Reconstruction Transformer (LGRT). The LGRT is re- designed specifically for 3D avatar tasks, which demand finer granularity compared to SLAM-based environmental reconstruction. Moreover, the human subjects captured for 3D avatars cannot remain perfectly static and often exhibit rich dynamic characteristics (i.e., expressions, poses, etc.). The LGRT comprises stages: facial feature extraction, face encoding, face aggregation and registra- tion, 3DGS attribute generation, canonical 3DGS model fusion, and 3DGS rasterization.
Face Encoding FastAvatar encodes each face observation T; to set of token æ; through DI- NOv2 NOv2[Oquab et al. (023). These tokens vary from head poses, facial expressions to camera poses, and undifferentiated aggregation would lead to over-smoothing and aliasing effects. Therefore, Fas- tAvatar introduces three critical encodings to label distinct facial tokens, facilitating subsequent aggregation. This process be formulated as:
h4 2 (zx, MLP(( 2F 2 22)
(2)
where ul(-) denotes concatenation along the dimensional axis. h, denotes the encoded face kens. Tss 2psse and 2 represent the camera pose, head pose, and expression coefficients of x; respectively. These are processed through lightweight MIP layer for feature alignment and dimen- sionality alignment. We obtain rough initial estimates the three parameters through multi-view FLAME tracking.
Face Aggregation and Registration The core enabling component for constructing dense 3D avatar from variable-length input data lies in the aggregation and registration of face tokens. The purpose of aggregation extract intra-token features while incorporating initialized 3D positional

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prompts. These positional prompts provide the LGRT with initial 3DGS positions, thereby accel- erating 3D reconstruction. As illustrated in Figure aggregation is implemented through frame attention, composed of dual-stream DiT blocks Labs al. (0225;; Labs (2024). which aggregates intra-token information while fusing 3D positional prompts. Face token registration serves as the fundamental operation for fusing multiple inputs. In Figure 2] this is achieved via global attention, which aligns encoded face tokens to achieve 3D spatial registration and fusion. To enhance quality and accelerate convergence, we initialize our frame attention using weights from LAM's blocks. Global attention and frame attention are interleaved in cascaded architecture, with total of L pairs employed to process face tokens, ultimately yielding tokens suitable for generating the 3DGS representation (ho.".. _NN]-
Canonical 3DGS Model Fusion Following the aggregation and registration through global atten- tion and frame attention, we obtain processed tokens hú, corresponding to each frame. These features are then fed into a GS Head (i.e., a two-layer MLP with shared weights across tokens) to predict the target 3DGS attributes 9:- The point cloud of derived by driving 9; through Linear Blend Skinning (LBS) expression deformation is then rendered via Gaussian splatting obtain the reconstructed face 7. Our approach extends beyond gs: we further aggregate all go. "
9 ((1. 02, ..N).
(3)
The fused 95 integrates unique information from all perspectives (e.g.. multi-view observations, di- verse expressions), achieving optimal reconstruction quality. However, naive fusion would cause point cloud misalignment, ghosting artifacts, and color discrepancies. To address this, we intro- duce Landmark Tracking Loss and Sliced Fusion Loss to explicitly encourage proper alignment of Gaussian point clouds during aggregation and registration stages.
3DGS Pruning Although the 3DGS method achieves high-quality and real-time rendering, it of- ten suffers from redundant memory consumption due to its explicit structure and tends to be more prone to overfitting because of the lack of smoothness bias in the neural network. This is especially problematic in our incremental reconstruction scenarios, where the number of GS points increases linearly with the number of input frames, leading inefficient resource usage and limiting render- ing speed. To address this, inspired by LP-3DGS Zhang et al. (2024b) and MaskGaussian Liu et al. (2025) we apply Gumbel-Soffman Jang al. 2017) to sample one differentiable category, denoted as M, {0. Then we integrate masks directly within the rasterization framework, effectively decoupling Gaussian presence from attributes such as opacity and shape. This mechanism prunes over 50% of the GS primitives without degrading reconstruction quality, further improving render- ing efficiency. To prune redundant 3D Gaussian primitives, we apply an L1 regularization term to the trainable mask, encouraging it to become sparse, formulated as Lmask = 1 E.E, [m].
N
3.3 TRAINING STRATEGY
Sliced Fusion Loss To enable the model to take advantage of the richer information provided by multiple inputs, we introduce Sliced Fusion Loss, allowing G to handle arbitrary numbers of input frames. Specifically, during training, we randomly sample one frame from the input obtain single frame-wise Gaussian representation 9:. In parallel, we randomly select Nyticed frames from the input, where Nsticed is less than the total number of input frames for memory efficiency, and fuse them to construct multi-frame Gaussian representation Jslicod Both g; and Jsliced are rendered into RGB images using the camera parameters, expression coefficients, and head poses of all input and target frames, and the corresponding losses are computed.
I, = V (giz Tis 2, exp pose)
2
(4)
isieed = V (goticed mey exp x pose).
(5)
The overall loss function consists of two components: one supervises the rendering quality of the constructed 3D Gaussian head, and the other supervises the combination of frame-wise Gaussian representations to ensure consistency across frames.
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Method
View
PSNR
SSIM
LPIPS
Identity 4
FPS f
Modeling Time (s)
LAM
17.30
0.773
0.149
0.135
125
0.31
MonoGaussianAvatte
11.83
0.631
0.620
0.432
<10
> ~100
Avatars
16.35
0.740
0.332
0.299
<0
100
FastAvatar
20.08
0.860
0.143
0.116
240.17
1.33
Method
Views Identity
PSNR
SSIM
LPIPS
FPS f Modeling Time (s) L
LAM
16.69
0.743
0.204
0.167
45
0.39
Avaaaa
12.71
0.798
0,437
0.368
>100
GaussianAvatars
17.52
0.802
0,340
0.278
<10
> 100
FastAvatar
22.12
0.880
0.094
0.098
101.62
4.22
Method
Views Identity & FPS f Modeling Time (s) L
PSNR
SSIM 1 LPIPS
LAM
16.59
0.718
0.235
0.206
24
0.43
Avaaa
13.11
0.650
0.493
0.298
<10
> 100
Gaussian Avatars
20.35
0.820
0.320
0.252
<10
100
FastAvatar
22.19
0.880
0.093
0.097
52.28
8.56
Method
16 Views
PSNR
SSIM 1 LPIPS
Identity
FPS f Modeling Time (s) L
LAM*
16.49
0.697
0.265
0.238
0.69
MonoGaussianAvatarr
15.81
0.721
0,406
0.202
<10
>100
GaussianAvatars
21.48
0.873
0.281
0.185
<10
>100
FastAvatar
22.29
0.881
0.092
0.095
17.65
26.06
Table I: The quantitative comparison among FastAvatar, LAM He et al. (2025), MonoGaussinnA-
vatar Chen al. (2024c), and GaussianAvatars Qian et al. (2024a) includes critical metries: Re- construction quality (PSNR, SSIM, LPIPS); Modeling time: Duration required to reconstruct the 3DGS model; Inference speed: Animation rendering FPS of the output 3DGS model.
Pixel Losses
The rendered RGB images are supervised using photometric losses against the cor- responding ground truth target images:
Lrgb i,, Tot
| T9t
Lesim SSIM(i, 18t) SSIM 19t).
(7)
We also incorporate perceptual losses to encourage the emergence of more high- freg-freyeency details:
Lipips = LPIPS(I,. 1s4) + LPIPS(I lst).
Landmark Tracking Loss Unlike novel view synthesis, canonical space registration is super- vised directly on the input frames. The landmark tracking loss is introduced to encourage precise localization of facial landmarks throughout the input images:
Ltrack
Mu 2R
(9)
Our total loss defined as follows: L = \ι Lrgb + Xe + AaLipips + Xa Ltrackk + As Asmask
(10)
with 0.8, = = 0.1, As and As 0.0005.
EXPERIMENTS
4.1 TRAINING
We train FastAvatar on a multi-task dataset derived from /ww.eeeeeeeeeeeeeeeeeeaeooo Kirschstein et al. a (2023), which contains multi-person, multi-camera videos with wide range of facial expressions. To encourage

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Published as a conference paper at ICLR 2026
adaptability to diverse imput settings, the dataset includes both monocular and multi-view subsets. Input-output pairs are constructed by sampling 16 frames each, either from a single video or from 12 camera views of the same subject. For each pair, a random subset of to 16 input frames is further selected, enabling the model to robustly handle scenarios with sparse or varying numbers of input frames such real-world recordings with incomplete or irregular camera captures.
4.2 EXPERIMENTAL SETUPS
Task. We evaluate the model's ability to generate a 3D head avatar for unseen subjects from vari- ous types of input, including single image, an unordered and arbitrary number of monocular video frames, and multi-view frames.
Metries. We employ three paired-image metrics to measure the quality of individual rendered images: Peak Signal-to-Noise (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). We also evaluate identity preservation by computing similarity using ArcFace Deng et al. (2019): Chen et al. (2020 2024a) features.
Baselines. We compare FastAvatar with recent state-of-the-art systems for 3D head avatar gener- ation across various tasks, including reconstruction from single image, monocular video frames and multi-view frames. LAM He et al. (2025) A model that generates one-shot animatable Gaus- sian heads using canonical Transformer with point-cloud representation and multi-scale cross- attention, enabling real-tine hilltime. expression-consisee
avatar animation and editing from single im- age. Avat3r Kirschstein et al.] (2025) system for regressing animatable 3D head avatars from limited multi-view images by combining large reconstruction and foundation models with cross- attention layers to effectively model 3D facial dynamics and generalize across diverse data. Mono- AaassianAvatar Chen al. (2024c) and GaussianAvatar Qian et al. (2024a) are two representative optimization-basee 3D Avatar methods, both of which use FLAME as a proxy 3D model, similar to ours.
We conduct all comparison experiments on the same 48GB Nvidia RTX 4090 GPU. To ensure a fair comparison, we slightly modify the LAM renderer for single-shot input, enabling it fuse information from multiple frames like FastAvatar, while keeping all other components consistent with the official repository. We retain the original model weights and confirm that its performance faithfully reflects the official version. We refer to this variant as LAM* in the following. Notably, with only I input frame, LAM* automatically reverts the official LAM, which is designed for single-frame inputs, ensuring fair comparison.
4.3 QUALITATIVE COMPARISON
Comparative results against LAM, MonoGaussian, and GaussianAvatar are presented in Figure 3 We evaluate 4 distinct input configurations (1, 4, 8, and 16 views) by progressively increasing the number input frames. Key observations indicate that while LAM yields performance roughly on par under kinle-view conditions, it fails to benefit from additional input views due to the lack of registration. Conversely, optimization-bans based methods exhibit significant performance degradation with sparse inputs (e.g., view), though their reconstruction quality improves progressively more views become available. FastAvatar consistently outperforms the baseline across all view settings (1~16 views), while further enhancing its ability to capture fine-grained details-such teeth gaps, wrinkles, and acne-as the number of views increases.
4.4 QUANTITATIVE COMPARISON
Table presents quantitative comparison of the four methods. All approaches demonstrate sub- stantial improvements in reconstruction metrics with increasing input frames, with both MonoGaus- sianAvatar and GaussianAvata. exhibiting gains in both subjective assessments (i.e., LPIPS) and objective metries (i.e., PSNR, SSIM). This reaffirms the critical importance of richer input data for high-fidelit reconstruction. However, LAM shows an inverse relationship: as its input views in- crease, quantitative performance degrades, which can also be illustrated in Figure Although LAM achieves impressive visual quality with single image (LPIPS: 0.149), its generative bias introduces pose and expression artifacts that compromise objective measurements. FastAvatar achieves highly
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Frame
4 Frame
8 Frame
16 Frame
MonoGaussian Avatar
Gaussian
F'astAvatar Frame
FastÁvatar
FastAvatar
FastAvatar
LAM
GT
Avatar
4 Frame
Frame
16 Frame
Figure 3: We benchmark FastAvatar against representative optimization-bant methods (Mono- Gaussian Avatar Chen et al. (2024c), GaussianAvatar Qian et al. (2024a)) and feedforward ap- proaches (LAM He et al. (025)). Our results demonstrate the performance evolution across meth- ods as the number of input views (referring to input images number) increases. Please zoom for better view.
competitive growth across both subjective and objective dimensions. validating core hypothesis. Through architectural and training innovations, FastAvatar establishes an optimal equilibrium be- tween generative capability (hallucinating plausible details under sparse inputs) and reconstruction fidelity (strict adherence to observed data given sufficient views).
4.5 INCREMENTAL RECONSTRUCTION
FastAvatar enables incremental improvement by accepting input sequences of any length and order. As more observations are added, the reconstruction quality continues to improve. In contrast, exist- ing methods often require fixed number of input frames, which reduces flexibility and may result in data loss. FastAvatar's incremental design thus ensures both versatility and efficient data usage.
As illustrated in Figure 4 method achieves superior expressiveness and rendering fidelity in avatar generation compared to the baselines, and demonstrates robust performance even for subjects wearing accessories.
Moreover, increasing the number of input views further improves the reconstruction of fine- fine-grained details, such hair and teeth gaps. This incremental reconstruction allows the model overcome the limitations of restricted viewpoints by leveraging more informative input frames-a capability that is difficult achieve for models with fixed input forms. For example, in Figure the character's left-ear earring is not sufficiently observed with small number of input views, but it is reliably reconstructed as the number of views increases.
Unlike the sparse and randomly sampled data used in our main experiments, real- sequences are highly continuous. Processing all frames with Global Attention imposes prohibitive computa- tional and memory costs. A naive solution is to uniformly sample 16 frames, but this risks missing important information present in the remaining frames. Inspired by FramePack Zhang et al. (2025). retain the 16 uniformly sampled frames as sparse inputs and compress all remaining frames into an aggregated token representation. which is then treated as an additional input. This preserves com- plementary details while keeping computation tractable, enabling FastAvatar to process hundreds of frames in a single feed-forward pass. Figure 5 shows that adding the compressed frames improves fine-grained details.
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Driver
View
View
9 View
View
FastAvatar
LAM
Figure 4: Reconstruction quality as the number of input observations increases. More observations improve reconstruction quality.
Tremme Packing
Uniform Sampling
23.00
PSNR
00M
21.65
GT
16 View
512 View
Figure 5: Performance on longer input sequences. Starting from strong reconstructions using only the 16 sparse input frames, incorporating the compressed additional frames further enhances fine- grained details (e.g.. the oral cavity, which is absent in most frames). While uniform sampling fails achieve this improvement, feeding all frames leads to OOM.
4.6 MULTI-VIEW OBSERVATIONS RECONSTRUCTION
To further evaluate FastAvatar's performance on the Multi-view Observations Reconstruction task, we create multi-view few-shot 3D head avatars for subjects from the Ava256 Martinez et al.) (2024) dataset, which was not used during training. We compare our results with those of the state-of- the-art method Avat3r Kirschstein al. (2025) and use the results provided in its original paper to ensure fair comparison (since its implementation is not publicly available, we ensure fairness by directly using results from the Avat3r paper). The qualitative results are shown in Figure Note that only used images from Ava256 for tracking and to obtain FLAME parameters, without utilizing the provided informed encodings. Nevertheless, FastAvatar still achieves highly competitive results and benefits from additional multi-view inputs. producing more detailed reconstructions. Close inspection reveals that Avat3r fails to preserve facial identity accurately, reconstructing consistently wider facial structures than observed in source inputs.

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GPAvatar
Avat3r
Ours 1-in
Ours 4-in
Ours 8-in
Ours 16-in
GT
Figure 6: Visual comparison with Avat3r and GPAvatar Chu et al.
2024
Please zoom in for
clearer view.
Method
I View
View LPIPS
SSIM
Identity
SSIM 1
Identity
(K)
w/o sliced fusion loss
0.0373
20.47
0.861
0.123
0.158
20.7
0.0345
21.69
0.857
0.131
0.138
82.8
w/o tracking
0.140
0.172
13.0
0.0322
21.64
0.866
0.120
0.128
41.2
wlo global attention
0,0922
15.40
0.760
0.238
0.400
10.3
0.0462
19.49
0.828
0.162
0,270
40.1
fussion
wlo GSS pruning
0,0380
20.32
0.857
0.137
0.144
21.7
0.0322
21.67
0.868
0.112
0.130
86.7
Ours
0.857
0.136
12.8
0.0303
21.86
0.871
Method
8 View
16 View I.PIPS
Identity 4
Identity
(K)
w/o sliced fusion loss
0.0386
21.12
0.849
0.144
0.151
165.4
0.0417
20.62
0.839
0.159
0.180
330.5
w/o tracking
21.61
0.867
0.119
0.124
78.6
0.0322
0.865
0.129
wlo glohal attention
0,0413
19.97
0,838
0.156
0.223
78.0
0.0408
20.06
0.830
0.167
0,210
158.7
fussion
wio GS pruning
0.0326
21.63
0.868
0.110
0.130
173.4
0.0327
21.61
0.867
0.110
0.136
346.8
Ours
0.0297
0.871
0.103
0.0293
22.04
0.118
Table 2: Ablation studies key components of FastAvatar. The appendix visualizations strongly recommended for better understanding.
4.7 ABLATION STUDY
To validate the effectiveness of each component in our method, we conduct both quantitative and qualitative ablation studies. The qualitative visualizations provided in the appendix for space considerations. As shown in T Global Attention is crucial for coordinating inter-frame infor- mation, while GS Fusion aggregates the GS points from each frame into unified representation. Sliced Fusion Loss and Tracking Loss supervise GS registration, enforcing structural consistency and temporal coherence. Without these components, newly introduced frames fail to provide re- liable information, leading to degraded registration and blurred outputs as the number of frames increases. Meanwhile, Gaussian Pruning removes redundant primitives, slightly improving per- formance while substantially accelerating rendering. Together, these mechanisms ensure effective inter-frame coordination, accurate registration, and efficient rendering.
5 CONCLUSION
In this paper. we present FastAvatar, feed-forward 3D avatar reconstruction framework capable of constructing a high-quality animatable 3DGS avatar within seconds. Distinct from existing ap- proaches, FastAvatar demonstrates unique capability for incremental avatar reconstruction flex- ibly leveraging incoming observations to progressively enhance reconstruction quality. We contend this represents promising research trajectory. Three pivotal innovations enable this functional- ity: Alternating Attention, augmented with fine- -ne-grained expression and pose encodings, achieves high-precision registration of unordered data; The proposed Landmark Tracking Loss and Sliced Fusion Loss facilitate robust fusion of multiple 3DGS representations for superior modeling fidelity. Experimental validation confirms FastAvatar's potential in these dimensions.
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A
LLM USAGE
Large Language Models (LLMs) used solely to assist in refining the manuscript's language, improving readability, and ensuring clarity, including sentence rephrasing and grammar checking. The LLM did not contribute to the research ideas, methodology, experiments, or data analysis. The authors take full responsibility for the scientific content and confirm that all LLM-assisted text adheres ethical guidelines, with contribution to plagiarism misconduct.
B ETHICS STATEMENT
The proposed FastAvatar framework follows the same data assumptions and usage boundaries as existing 3D avatar reconstruction and neural rendering methods. It does not introduce new mecha- nisms that lower the barrier unauthorized identity reconstruction, nor does relax requirements on input data quality. In practice, the method still relies on clean and identity-consistent multi-frame input, which inherently limits large-scale or covert misuse.
All experiments are conducted on publicly available datasets with appropriate licenses, and no pri- vate or sensitive data are collected. The intended applications of FastAvatar lie areas such as AR/VR telepresence, digital content creation, and numan-computer interaction, where user consent typically explicit. We emphasize that responsible deployment requires ensuring that the method not applied to reconstruct individuals without consent to generate deceptive impersonating content.
C REPRODUCIBILITY
In this section, we provide more implementation details of FastAvatar, including data preparation and model architecture. Furthermore, code will be released after the paper is accepted.
C.I DATA PREPARATION
Our training utilizes the Nersemble dataset. Initially, FLAME tracking is applied to extract FLAME parameters and camera poses, which serve as inputs for subsequent training stages. From the original Nersemble data, we extract 521 distinct video clips, and sample the frames at 15 FPS. Cameras with poor face tracking quality were excluded, remaining 12 cameras. The processed data was sampled twice to construct the final dataset: first, sampling frames within the same video sequence, and second, performing random sampling across all shots of the same action sequence. These two sampling strategies collectively support training the unified task. To enhance the stability of training and testing, we randomly assign the processed figures' backgrounds to black, white, or gray. Note that, to enhance the model's generative capability, all expression parameters, poses, and related data used during inference differ from the input data.
Hyperparameter
Value
DINOv2 patch size
x
#expression token MLP layers
2
Encoder
#camera token MLP layers
Expression Token MI.P activation
GELU
Camera Token MLP activation
GELU
Output dimension Input image resolution
1024
504 x 504
#Frame Attn Layers
10
#Global Attn Layers
AA
Hidden dimension
1024
Order
[Global, Frame]
Table 3: Hyperparameters. Where AA represents Alternation Attention
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Noise
L1L
PSNRT
SSIM
LPIPS]
Identity 4
px
0.0264
22.50
0.873
0.096
0.100
px
0.0268
22.38
0.872
0.098
0.099
0.0273
22.22
01.870
0.098
0.103
16 px
0.0277
22.10
0.369
0.099
0.102
32 px
0.0280
22.02
0.869
0.099
0.105
Ours
0.0263
22.50
0.872
0.096
0,099
Table 4: Ablation studies on FLAME tracking. We evaluate the robustness of FastAvatar under
varying levels landmark perturbation during FLAME tracking.
The accuracy of FLAME tracking primarily depends on the precision detected facial landmarks, the FLAME parameters typically estimated by optimizing the model to fit these landmarks. However, the reliability of such proxy models (e.g., FLAAME and other 3DMMs) is inherently con- strained by factors such as limited representational capacity and sensitivity to landmark quality. To assess how these factors affect our method, we include an additional ablation experiment that in- jects controlled noise into the facial landmarks. The results (Table demonstrate that FastAvatar remains robust under reasonable perturbations, indicating that strict accuracy in FLAME tracking is required.
C.2 TRAINING
In table 3, we present the most important hyperparameters for training FastAvatar. We train the model by optimizing the training loss with the AdamW optimizer for 150K iterations. We use cosine learning rate scheduler with learning rate of 4e-5. The training runs on 8 H100 GPUs over 14 days. The substantial accumulation of Gaussian points across multiple input frames leads to high GPU memory consumption during training. To address this, we adopt bíloat16 precision and gradient checkpointing for improved memory and computational efficiency.
C.3 FRAMEPACK
While sparse frames suffice for high-quality 3D head reconstruction, quality degrades when viewpoint coverage is incomplete-e.g. mouth-opening expressions fail without intra-oral views. However, full global attention over all frames is prohibitively expensive.
We address this with two-tier token design inspired by FramePack. We designate 16 base frames whose DINO features are kept at full spatial resolution, and compress all remaining frames with a learned k strided convolution (k=8), reducing each to 1/k² of its original token count. Within each alternating-atteniin image tokens independently- base frames full resolution, compressed frames through sepa- rate set of weights at reduced resolution. Global attention then concatenates all tokens across both tiers and applies RoPE-based self-attention jointly. This provides coverage from arbitrarily many extra viewpoints at cost sublinear in total frame count, enabling incremental reconstruction from hundreds of input frames.
layer, frame attention cross-attends each frame's point cloud with its own
D MORE RESULTS
In this section, present additional results of FastAvatar, including its performance a broader set of video sequences and its generalization real-world daily-captured data.
More Comparison We provide additional qualitative results comparing FastAvatar and baseline models in both self-reenactment and cross-reenactment settings. As shown in Figure 15] Figure 16 and Figure [4] FastAvatar outperforms the baselines. Optimization-base 3D avatar methods fail to achieve satisfactory results with sparse inputs, while LAM often exhibits unrealistic details and significant pose inaccuracies. The advantage of FastAvatar becomes even more evident in the cross- reenactment setting, where the subject's 's identity and camera pose exhibit large discrepancies. We further evaluate FastAvatar against additional competitive methods. The results are presented in Figure 7 and Figure
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GT
Arc2Avatar
SynShot
LAM
FastAvatar
Figure 7: Qualitative results the INSTA dataset. LPIPS scores: Ours (0.1332). LAM (0. (4.1,,9), Arc2Avatar (0.4585), SynShot (0.1523). Identity scores: Ours (0.076), LAM (0. (0.124), Arc2Avatar (0.411), SynShot (0.115).
GT
Arc2Avatar
HeadGap
LAM
FastAvatar
Figure 8: Qualitative results on the Nersemble dataset. LPIPS scores: Ours (0.1267), LAM (0. (0.1) Arc2Avatar (0.4665), HeadGap (0.1592), Identity scores: Ours (0.070), LAM (0.097), Arc2Avatar (0.308), HeadGap (0.101).
Generalization to Wide-Range Viewpoints The Nersemble training set contains only 12 con- strained camera views. To evaluate the robustness of our method, we test it on much wider range of viewpoints. As shown in Figure 12 the model maintains high-fidelity reconstruction across all novel views, demonstrating strong wide-range generalization. For comparison, we include the re- sults of LAM in Figure [3] The results demonstrate that FastAvatar outperforms the tfateoof-hhe-at across a wide range of viewpoints.
Ablation Study We further highlight the role of the key components incremental reconstruc- tion. As illustrated in Figure removing these components prevents fine details from being properly aligned, leading to noticeable artifacts and blurred regions. Although the landmark tracking loss only supervises 68 facial landmark points, it still provides strong guidance for Gaussian registration, effectively assisting the model in aligning new frames during incremental updates. Together with the Sliced Fusion Loss, it ensures that additional observations consistent refinement of the reconstructed avatar. Global Attention enables the model to leverage inter-frame dependencies, integrating complementary features from multiple frames; without formation remains localized, and cross-frame consistency cannot be achieved. GS Fusion consol- idates per-frame Gaussians into coherent representation, allowing the model maintain consis- tency across frames. Finally, Gaussian Pruning removes redundant primitives, slightly improving performance while significantly accelerating rendering, enabling efficient incremental updates even for long sequences.
be accurately fused, enabling
tracking loss
w/o Global
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GS Praning
Figure 9: Comparison of visual effects of model reconstruction after removing the key components.
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FastAvatar
Figure 10: Typical failure cases: FastAvatar relying on LBS and FLAME- encodings, struggles with complex facial muscle dynamics, fine-go fine-grained details (e.g.. wrinkles), eye-gaze movements, and structures outside the FLAME topology such as the tongue.
GT
+1 Chunk
Chunk
Chunk
+4 Chunk
Chunk
Figure 11: As the streaming input progressively incorporated, the reconstruction of the oral ity-largely invisible in most chunks chunks-graduullly improves while structural consistency is main- tained in other regions, enabling incremental reconstruction.
Streaming Incremental Reconstruction FastAvatar is also capable of streaming incremental re- construction, meaning the model continuously updates and refines the 3DGS representation as new video frames are received. To achieve this. we adopt sliding-window approach, where each win- dow contains 16 frames with a 4-frame overlap for registering incoming frames. Thanks to the Alternating Attention design, new frames build upon the Gaussians predicted by the previous model to produce more informative reconstructions. 1 demonstrates this: starting from a single-view image, as additional views provided (excluding the test view), reconstruction qual- ity improves overall, including at previously unseen angles, thus realizing streaming incremental reconstruction.
First, our method relies on LBS and FLAME-based encodings to drive 3D head avatar motion, which limits the representation complex facial muscle dynamics. As a result, the model has difficulty reproducing fine-grained muscle-dependent details such wrinkles and also cannot accurately capture eye-gaze movements, often defaulting to an average direction. Furthermore, because the Gaussians are anchored to FLAME vertices, the model is unable to represent structures outside the FLAME topology, including the tongue. Figure IO presents representative failure cases.
Limitations
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-10%
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Figure 12: Generalization wide-range viewpoints. FastAvatar achieves high-fidelity reconstruc- tion across 14 novel viewpoints that are entirely outside the training set.
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Figure 13: The performance of LAM on wide-range viewpoints.
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Input/ Driver
MonoGaussian Avatar
FastAvatar View
FastAvatar View
FastAvatar View
FastAvatar
LAM
16 View
Figure 14: Additional Comparisons with Baseline Methods (cross-reenactment).
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LAM
MonoGaussiaAAvttar
GaussianAvatars
FastAvatar
Figure 15: Additional Comparisons with Baseline Methods (self-reenactment part 1).
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LAM
MonoGausiiaAAvatar
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FastAvatar
Figure 16: Additional Comparisons with Baseline Methods (self-reenactment part 2).
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Reviews:
[{"review_id": "FzEWb9Hsma", "reviewer": "Reviewer_rdbM", "summary": "FastAvatar is introduced as a novel feedforward framework for 3D avatar reconstruction, designed to overcome existing challenges in high time complexity, sensitivity to data quality, and inefficient data utilization in contemporary methods.\n\nThis single unified model flexibly leverages diverse daily recordings, such as a single image, multi-view observations, or monocular video, to reconstruct a high-quality animatable 3D Gaussian Splatting (3DGS) model typically within seconds. The central component of the framework is the Large Gaussian Reconstruction Transformer (LGRT), which is specifically adapted from the VGGT structure to meet the demanding registration and aggregation needs of 3D avatar tasks. The LGRT incorporates designs including a 3DGS transformer that aggregates multi-frame cues by injecting initial 3D positional prompts, and multi-granular guidance encoding utilizing camera pose, expression coefficients, and head pose to mitigate animation-induced misalignment for variable-length inputs. Crucially, the model utilizes a Landmark Tracking Loss and Sliced Fusion Loss during training, which supervise the combination of frame-wise Gaussian representations to ensure consistency and enhanced aggregation accuracy.\n\nIntegrating these contributions, FastAvatar uniquely pioneers incremental 3D avatar reconstruction, allowing the model to continuously ingest new observational data to progressively refine modeling quality while achieving highly competitive quality and speed compared to existing state-of-the-art approaches.", "questions": "The specific contribution of the initial 3D prompt, which distinguishes this approach from other Transformer designs, is not quantitatively demonstrated. The description is unclear: is the LGRT initialization based on standard FLAME vertices, subject-specific identity-shaped FLAME, or another form? Clarification and discussion would be helpful.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- FastAvatar uses a single unified model capable of processing diverse daily recordings, including a single image, multi-view observations, or monocular video. FastAvatar demonstrates greater model flexibility and higher data utilization efficiency compared to other feedforward methods like LAM or Avat3r.\n\n- Sliced Fusion Loss is a key component of the FastAvatar framework. This loss enables the model $G$ to leverage richer information from multiple inputs and to handle an arbitrary number of frames. It supervises the fusion of per-frame Gaussian representations, ensuring cross-frame consistency.\n\n- FastAvatar introduces the capability for incremental 3D avatar reconstruction, a feature currently unattainable by existing approaches. This means the model can continuously ingest new observational data to progressively and reliably refine modeling quality.", "weaknesses": "- Although the method handles variable lengths, the practical input size N is explicitly limited. This constraint (max 16 frames) suggests that processing longer videos (which previously required 30 seconds at 25fps, for optimization-based methods) still requires sampling or chunking, potentially limiting true incremental modeling for extended footage.\n\n- The precision of these proxy models (FLAME/3DMM) is known to be sensitive to limitations like representational capacity and data quality, often failing to produce highly accurate proxy 3D models. Although FastAvatar uses this information for alignment, the quality of the final reconstruction is fundamentally tied to the accuracy of these initial estimates. The current ablation study only focuses on the two proposed losses ($L_{track}$  and $L_{sliced}$). It is recommended to include an ablation on the estimated FLAME parameters. Measuring performance degradation would quantitatively assess robustness to real-world FLAME tracking inaccuracies. For example, the authors could test different tracking methods. When tracking is erroneous, does the method propagate these errors, or can the multi-frame image evidence mitigate them?", "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": "k95QoqrxFN", "reviewer": "Reviewer_WZHK", "summary": "The paper proposes FastAvatar, a feedforward framework for fast and unified 3D avatar reconstruction from variable-length inputs (single images, monocular sequences, or sparse multi-view frames). The core model, termed LGRT, uses alternating frame/global attention blocks and injects camera pose, expression, and head-pose encodings into image tokens. The model directly predicts 3DGS attributes for avatar reconstruction, while two new losses, including sliced fusion loss and landmark tracking loss, encourage consistent multi-frame fusion and geometric alignment. The paper claims incremental reconstruction capability, where quality improves as more observations are provided.", "questions": "- During fusion, how exactly does the Gumbel-Softmax operate? Does it directly set the Gaussian attribute masks for some frames to zero? Please provide more details about its mechanism.\n- As the number of input images increases, the performance improvements are limited; interestingly, in Figure 4, the visual results become sharper with more input views, which contradicts the common expectation that multiple views usually cause smoothing. Could this be related to some Gaussian attribute fusion strategy? Where do the authors think this gain originates from?\n- Training time? The paper does not report key training details such as the hardware configuration, batch size, training time, or number of GPU hours required for training.\n- How many Gaussians are predicted per frame and per fused model? Are they anchored to FLAME vertices or generated freely in canonical space?\n- How are LBS weights for Gaussians derived—learned, interpolated from FLAME, or transferred directly?\n- What does “incremental” precisely mean? Does the system reuse previous results or recompute from scratch with new inputs?\n- Why was LAM modified for multi-frame input, and how was fairness ensured in this comparison?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Practical motivation: Addresses fast, unified avatar reconstruction from variable-length inputs without per-identity optimization.\n\n- Architecture: Extends VGGT with multi-granular encodings (pose, expression, camera) suitable for dynamic faces.\n\n- Loss design: The sliced fusion and landmark tracking losses are reasonable to promote frame consistency and alignment.\n\n- Feedforward inference potentially enables better avatar generation compared to optimization-based methods.", "weaknesses": "- Dependence on external camera pose tracking:\nUnlike VGGT, which infers relative geometry and camera pose implicitly through attention, FastAvatar requires explicit camera parameters and FLAME-derived head/expression tracking as inputs. This reliance on external preprocessing weakens the method’s claim of being a fully feed-forward, generalizable system. In practice, the need for accurate tracking limits applicability in real-world scenarios (e.g., in-the-wild videos) and reduces the robustness advantage that a unified transformer architecture should ideally provide.\n- Methodological inconsistency: Despite criticizing parametric proxies, the method depends heavily on FLAME tracking for pose/expression priors, limiting robustness and novelty.\n- Visual evidence lacking:\nThe qualitative effects aren’t striking. Some reconstructions lack detail and natural dynamics (see video).\nAll qualitative examples show mainly frontal or near-frontal views, without large-angle or side-view results. This omission makes it difficult to assess whether the method truly captures consistent and complete 3D geometry rather than merely fitting frontal appearances. Given that the paper claims to produce fully animatable 3D avatars, the absence of wide-angle and rotational visualizations significantly weakens the empirical validation of this claim.\n- Incremental claim overstated: The model recomputes from all available inputs rather than performing genuine streaming updates.\n- Evaluation issues:\n   - Modified baselines (e.g., LAM) may yield unfair comparisons.\n   - Missing key baselines such as Arc2Avatar, HeadGap, and SynShot.\n   - no identity evaluations.\n   - The video results are too short.", "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": "odkIdCZ51W", "reviewer": "Reviewer_Fo9q", "summary": "The paper presents a feed-forward 3D avatar head reconstruction framework that performs high-quality 3DGS-based modeling within seconds from arbitrary-length inputs (single/multi-view or video). They propose Large Gaussian Reconstruction Transformer (LGRT), which integrates multi-granular positional encoding (camera, expression, pose), and Landmark Tracking Loss and Sliced Fusion Loss, enabling incremental reconstruction. Extensive experiments on multiple datasets demonstrate that FastAvatar achieves superior reconstruction quality and rendering fidelity compared to state-of-the-art works.", "questions": "What are typical failure cases observed in reconstruction or animation (e.g., inconsistent geometry, occluded regions, or identity drift) in this work?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The proposed LGRT architecture is effective in aggregating multi-view / multiple image cues and aligning variable-length inputs, achieving consistent geometric and appearance coherence across frames.\n- The method shows substantial quantitative improvements in PSNR/SSIM/LPIPS, outperforming existing baselines across all view settings (1, 4, 8, and 16 frames).", "weaknesses": "- The tracking loss (Eq. 9) includes the term $y$, but its definition is missing. It should explicitly state how ground-truth and predicted landmarks $y_{j,i}$, $\\hat{y}_{j,i}$ are obtained.\n- Similarly, Eq. 10 introduces $L_{\\text{mask}}$, but no definition or explanation of this loss term is provided. A clarification of its role and formulation is necessary.\n- The framework claims to support arbitrary input lengths, but experiments are limited to at most 16 views. It is unclear whether the model generalizes beyond 16-frame inputs or whether GPU memory becomes a constraint.\n- The paper highlights incrementality as a core advantage; however, quantitative improvements saturate after 4 views (Table 1). The authors should discuss why the performance gain diminishes beyond 4~8 views.\n- In Table 2, the ablation only considers fixed-view settings. Since Sliced Fusion Loss is designed for multi-input benefit, it would be informative to show how its impact scales with increasing frame counts (e.g., 1, 4, 8, 16 frames w/ or w/o the Sliced Fusion Loss).\n\nI will reconsider the score when all those concerns are handled well.", "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": "FYlPyCPBwG", "reviewer": "Reviewer_8Upx", "summary": "This paper presents a feedforward framework for rapid 3D avatar reconstruction based on 3D Gaussian Splatting (3DGS). It can generate high-quality avatars from diverse inputs (single image, multi-view, or video) within seconds using a single unified model. The proposed Large Gaussian Reconstruction Transformer (LGRT) introduces (1) a 3DGS transformer for canonical reconstruction, (2) multi-granular guidance to handle pose and expression variations, and (3) incremental Gaussian aggregation for quality refinement. Experiments demonstrate that FastAvatar achieves good quality and speed compared to existing approaches.", "questions": "1. Why does your method achieve faster inference speed than LAM? Since multi-view fusion involves full attention and sampling operations, wouldn’t this introduce significant additional computational overhead?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Supports the reconstruction of high-quality 3D heads from various input types, including a single image, multiple frames, or multi-view observations.\n2. Designs a full-attention mechanism and a differentiable sampling strategy to effectively fuse image information from multiple inputs.\n3. Proposes a sliced fusion loss to further optimize reconstruction quality.", "weaknesses": "1. Ambiguous description: In Lines 200–209, the authors state that h_i represents the concatenated feature of a single image I_i with its expression z_i^{exp}. However, in Figure 2, h_i appears to denote the feature corresponding to different image/expression pairs under the same camera pose. The authors should clarify this inconsistency.\n2.  Missing ablation studies: In Lines 234–236, the authors claim that the naïve fusion strategy underperforms compared to their proposed Gumbel-Softmax sampling. However, no ablation study is provided to substantiate this claim. The authors are encouraged to include experiments quantifying the contribution of the Gumbel-Softmax sampling to the final performance.\n3. Lack of visual comparisons: The supplementary video only presents results from the proposed method. Including visual comparisons with other state-of-the-art approaches would more convincingly demonstrate the superiority of the proposed technique.\n4. Unclear contribution of key modules: It remains unclear how much the full-attention mechanism and the final Canonical 3DGS Model Fusion contribute to multi-frame aggregation. The authors are encouraged to perform an ablation study or provide quantitative evidence demonstrating their effectiveness. Since multi-view and multi-frame fusion form a core contribution of this paper, a more detailed analysis and discussion of these components would significantly strengthen the work.\n5. Lack of ethics statements.", "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)