Low-Rank Head Avatar Personalization with Registers
Authors: Sai Tanmay Reddy Chakkera
Aggelina Chatziagapi
Md Moniruzzaman
Chen-ping Yu
Yi-Hsuan Tsai
Dimitris Samaras
Venue: NeurIPS 2025 poster ()
Keywords: Personalization
Avatar Generation
Primary area: applications

Abstract: We introduce a novel method for low-rank personalization of a generic model for head avatar generation. Prior work proposes generic models that achieve high-quality face animation by leveraging large-scale datasets of multiple identities. However, such generic models usually fail to synthesize unique identity-specific details, since they learn a general domain prior. To adapt to specific subjects, we find that it is still challenging to capture high-frequency facial details via popular solutions like low-rank adaptation (LoRA). This motivates us to propose a specific architecture, a Register Module, that enhances the performance of LoRA, while requiring only a small number of parameters to adapt to an unseen identity. Our module is applied to intermediate features of a pre-trained model, storing and re-purposing information in a learnable 3D feature space. To demonstrate the efficacy of our personalization method, we collect a dataset of talking videos of individuals with distinctive facial details, such as wrinkles and tattoos. Our approach faithfully captures unseen faces, outperforming existing methods quantitatively and qualitatively.

Low-Rank Head Avatar Personalization with Registers
Sai Tanmay Reddy Chakkera Department of Computer Science Stony Brook University schakkera@cs wtw.:looosstoooooo.......o
Aggelina Chatziagapi Department of Computer Science
Stony Brook University echatziagapi@es @cs ooteepabooocssstttttoooooooo..o. edu
Md Moniruzzaman
Chen-Ping Yu
Yi-Hsuan Tsai
Atmanity Inc. mman@atmanity. 1o
Atmanity Inc.
Atmanity Inc.
cpyu@atmanity. io
yhtsai@atmani
Dimitris Samaras Department of Computer Science
Stony Brook University
samar sww.ybrook.oooooooooooooooooooo. camarras@ss.ssoryroook.. edu
Abstract
We introduce novel method for low-rank personalization of generic model for head avatar generation. Prior work proposes generic models that achieve high- quality face animation leveraging large-scale datasets of multiple identities. However, such generic models usually fail to synthesize unique identity details, since they learn general domain prior. To adapt to specific subjects, find that is still challenging to capture high-frequency facial details via popular solutions like low-rank adaptation (LoRA). This motivates to propose specific architecture, a Register Module, that enhances the performance LoRA, while requiring only small number of parameters adapt to an unseen identity. Our module is applied to intermediate features of pre-trained model, storing and re- re-purposing information in a learnable 3D feature space. To demonstrate the efficacy of our personalization method, we collect a dataset of talking videos of in- dividuals with distinctive facial details, such as wrinkles and tattoos. Our approach faithfully captures unseen faces, outperforming existing methods quantitatively and qualitatively. Project page:
Introduction
Synthesizing photo-realistic human faces has long been challenge for both computer vision and graphics. has broad applications from AR/VR, virtual communication, and video games, to the movie industry and healthcare. Earlier approaches rely 3D morphable models (3DMMs) (Garrido et al., 2015, 2014; Thies et al., 2016), while subsequent methods turn to generative adversarial networks (GANs) (Kim al., 2018; Pumarola et al., 2020; Prajwal al., 2020; Vougioukas et al., 2020). More recent works learn 3D neural representations of the human face, which rely on neural radiance fields (NeRFs) (Pumarola al., 2021; Park al., 2021a; Gafni et al., 2020; Park al., 2021b) or 3D) Gaussian Splatting (3DGS) (Kerbl et al., 2023; Cho et al., 2024; Qian et al., 2024; Xu al., 2024c). While these approaches lead high-quality results, they usually require identity- specific training and are not able to generalize. Only few recent methods propose generic models,
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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e.g. GAGAvatar (Chu and Harada, 2024), which preserve the high-quality rendering of 3DGS, while trained on a large-scale dataset of multiple identities, enabling generalization to unseen human faces.
However, such generic models usually fail to produce key identity-specific facial details, since they learn general domain prior. To produce distinctive details, prior work proposes adapting pre-trained model to a specific identity, e.g. through fine-tuning or meta-learning (Nitzan et al., 2022; Zhang al., 2023a; Saunders and Namboodiri, 2024). Low-rank adaptation (LoRA) (Hu et al., 2022) has been first proposed for large language models (LLMs). It injects trainable rank decomposition matrices into each layer of pre-trained model, leading to a significant decrease of the learnable parameters and on-par performance compared to fine-tuming tuning the entire model.
In this work, we address the problem of adaptation, also called personalization, to specific identity, which is not seen in the initial training of a generic model for head avatar generation. Due to its start with LoRA, by learning low-rank decomposition
efficiency and popularity in other fields, matrices for specific layers. We notice that LoRA is not sufficient to synthesize high-frequency facial characteristics (see Figure 1). Inspired by Darcet et al. (2023) that learn additional tokens (registers) in order to store global information for a transformer network, we propose a specific module that extends the idea of registers to 3D registers for human faces. To the best of our knowledge, we believe that this is the first method to extend registers to 3D representations.
design Register Module that learns 3D feature space that stores and repurposes information for a human face during training. Similar to registers in ViT (Darcet et al., 2023) that store global information of an image, our Register Module stores the distinctive details of an identity, given different views. We apply our Register Module to intermediate features that are extracted from a pre-trained DINOv2 model (Oquab al., 2023). While our proposed module can be applied to any network that uses DINOv2 features, we focus our study on GAGAvatar (Chu and Harada, 2024) as our generic pre-trained model since it a highly competitive method trained large-scale dataset (VFHQ Xie et al. (2022)) and achieves state-of-the-art results in the general avatar domain. To evaluate the efficacy of our low-rank personalizatton, we collect a dataset of talking videos of individuals with rare high- figh-frequency facial details, such as wrinkles and tattoos, that are not included in existing datasets. Our method outperforms of approaches. like learnnng and vanilla LoRA, both quantitatively and qualitatively, while it only requires a small number of parameters to adapt.
More specifically,
In brief, our main contributions are as follows:
We propose a novel method for low-rank personalization of a generic model for head avatar generation, that captures identity facial details.
We design a Register Module that stores and repurposes information for an identity in a learnable 3D feature space, extending the idea of registers for ViTs to 3D human faces.
We collect a dataset, namely RareFace-50, of talking videos of individuals with distinctive facial characteristics, e.g. wrinkles and tattoos, that are challenging to synthesize with generic models, and thus demonstrating the need for our method.
2
Related Work
Human Portrait Synthesis. Earlier approaches for video synthesis of human faces are based on 3DMMs (Garrido et al., 2015, 2014; Thies et al., 2016). A 3DMM (Blanz and Vetter, 1999) is parametric model that represents a face as a linear combination of the principal axes of shape, texture, and expression, learned by principal component analysis (PCA). Subsequent works propose GAN- based networks for video synthesis (Kim et al., 2018; Siarohin et al., 2019; Pumarola et al., 2020; Roich et al., 2023; Tancik al., 2021; Buchler et al., 2024) and audio-driven talking faces (Prajwal et al., 2020; Zhou et al., 2021; Vougioukas et al., 2020: Xu et al., 2024a). GANs are usually trained on large datasets of 2D videos of multiple identities, but they cannot model the 3D face geometry. More recent works leam 3D neural representations of the human face, which rely on neural radiance fields (NeRFs) (Mildenhall et al., 2020) or 3D Gaussian Splatting (3DGS) (Kerbl et al., 2023). Diffusion models have also become popular but they only produce 2D videos (Xu et al., 2024b) or are -peecity- (Kirschstein et al., 2024a). this paper, we explore personalization to capture facial details by adapting generic avatar model.

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Generic Avatar Model Adaptation
LoRA Registers (Ours)
Video Frames
Figure 1: Our method personalizes and adapts a generic head avatar model using LoRA, while preserving high-frequency identity-specific facial details using our Register Module and retaining the original inference speed. Note that the small image in the bottom-right corner is the driving image.
Animatable 3D Head Avatars. NeRFs have been first proposed for novel-view synthesis of static scenes (Mildenhall et al., 2020). They have been extended to dynamic scenes and human faces (Pumarola et al., 2021; Park et al., 2021a; Gafni et al., 2020; Park et al., 2021b; Chakkera et al., 2024). They usually represent human face by sampling 3D points in canonical space, which can be conditioned on 3DMM expression parameters to enable animation. Although they produce high-quality reconstructions, they require expensive identity-specifie training. Subsequent works (Zielonka et al., 2023; Duan et al., 2023) propose techniques to reduce the training and inference time. 3DGS (Kerbl et al., 2023) became very popular it achieves real-time rendering with high visual quality, by representing complex scenes with 3D Gaussians. It has recently been applied for dynamic human avatars (Cho al., 2024; Qian al., 2024: Xu et al., 2024c: Dhamo et al., 2024; Wang et al., 2025). However, most approaches learn identity-specifi models. Very few recent works propose generic models (Chu and Harada, 2024: Chu al., 2024; Kirschstein al., 2024b), which preserve the high-quality rendering of 3DGS, while trained on a large-scale dataset of multiple identities, enabling generalization to unseen human faces. However, generic models learn general domain prior and usually fail to produce unique identity-specine facial details, such as wrinkles or tattoos, as studied in this paper.
Personalization. Numerous works have proposed ways to adapt pre-trained models to various downstream tasks (Houlsby al., 2019; Zhang et al., 2023b). Parameter-efficient fine-tuning (PEFT) techniques are proposed to fine-tune large models efficiently. LoRA (Hu et al., 2022) adds low-rank matrices into each layer of pre-trained model, leading parameters and on- on-par performance compared to fine-tuning the entire model. In the context of face animation, fine-tuning part of the model has been utilized (Chatziagapi et al., 2024; Li al., 2025), as well as meta-learning, For instance, MetaPortrait (Zhang et al., 2023a) adopts meta- approach allow adaptation during inference, while Gao et al. (2020) uses meta-learning to adapt NeRF to a single image of unseen subject. Moreover, MyStyle (Nitzan et al., 2022) personalizes pre-trained StyleGAN by fine-tuning regions of its latent space, using set of images from an individual. Similarly, One2Avatar (Yu et al., 2024) adapts a generic NeRF to one or a few images of a person. TalkLoRA (Saunders and Namboodiri, 2024) applics LoRA for the task of 3D mesh animation, while My3DGen (Qi et al., 2025) adapts LoRA to the convolutional layers of StyleGAN2 in an EG3D-based network (Chan et al., 2022). Due to its popularity and efficiency, we study LoRA for generic model for head avatar animation. However, we find that LoRA is not sufficient to capture high-frequency facial details of a new identity. Thus, we propose to improve personalization by learning additional register module, inspired by register tokens in ViTs.
significant decrease of the learnable
Additional Takens in Neural Networks. Memory augmentation in neural networks goes back to long short-term memory (LSTT) units (Hochreiter and Schmidhuber, 1997) that store information through gates. Memory networks (Weston et al., 2014; Sukhbaatar et al., 2015) have access to external long-term memory. More recently, transformers have emerged powerful representation

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3DMH Riting
LoRA Expression Branch Layers
Renderer
LoRA
Cosree image
Fine Image
Driving Imagee
Froaan Patamntter
DINOY2
Register Module
Reconstructton Branch Layers
LoR4:
Trainable Parameters
Tmirabll LoRA Paramotara
dense
Source Image
Figure 2: Illustration of our Register Module in a generic avatar animation model. During adaptation, we pass the source image's DINOv2 features dense and driving image's 3DMM parameters to our module. Our module teaches the model to attend to specific regions in the dense DINOv2 features, thus providing better learning signals for LoRA capture identity-specific details. Note that the Register Module is not needed during inference but serves the register during LoRA training.
for various deep learning tasks, where the core element is self-attention (Vaswani et al., 2017). For language modeling, many works extend the input sequence of transformers with special tokens. Such additional tokens provide the network with 2019), or gather information for later downstream tasks, c.g. [CLS] tokens (Dosovitskiy et al., 2021), [MASK] for generative modeling (Bao et al., 2021). Unlike these works, Darcet al. (2023) present additional tokens as registers for storing and repurposing global information. Inspired by this, extend registers 3D feature space for human faces. We learn Register Module that stores information about distinctive high-frequency details of a human face.
information, e.g. [SEP] in BERT (Devlin al.,
Proposed Method
Figure 2 illustrates an overview our proposed framework that adapts a general avatar model to particular identity. Inspired by Parameter-Elffiiiet Fine-Tuning (PEFT), specifically LoRA (Hu et al.. 2022), we utilize LoRA to adapt the weights of a generalized avatar model to a particular identity. Through experiments, initially we find that adapting with LoRA does not sufficiently improve personalization (see Figure 1). Motivated by Darcet et al. (2023) that introduce registers in ViT' to store and repurpose global information, about identity-specie -specffc details. Our Register Module essentially teaches the model to attend to specific regions the dense DINOv2 features during adaptation. Importantly, it is only used during adaptation and is deactivated at inference time. With its guidance, the model learns to leverage DINOv2 features more effectively, enabling high-quality personalized head avatar generation with real-time speed from a single source image at inference.
propose Register Module to store information
Our proposed pipeline for personalizing generic avatar animation model consists of two main components:
(1) We add LoRA weights to specific pre- pre-trained layers of a generic avatar animation model (see Sec. 3.1), to keep the adaptation parameters efficient and to avoid catastrophic forgetting (see Sec. 3.2).
(2) We design a Register Module that learns a 3D feature space, facilitating the attention to specific regions of DINOv2 features, while adapting face from multiple views (see Sec. 3.3).
We first describe a generic avatar animation model in Sec. 3.1. Next, we describe the process of adding LoRA weights to pre-trained layers in Sec. 3.2. Finally, we describe the architecture of our Register Module in Sec. 3.3.
3.1 Preliminaries: Generic Avatar Generation
An avatar generation model consists of two branches: (a) reconstruction branch, and (b) expression branch. The reconstruction branch generates an animatable head avatar from the source image. The expression branch extracts the expressions and pose from the driving image which is used to animate the generated head avatar. These branches are merged and the output is rendered using neural renderer. This process learns a model for generalized head avatar reconstruction (see Figure 2).

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proc
SDHR FIEM
visti
brVingimage
ree
DROG
Pere I
Petamaan Tharable Paremndora
dense
Source Image
Figure 3: Illustration of our Register Module. We propose a Register Module that learns features in the 3D space. We rig embeddings to verrtices 3DMM mesh and camera pose pose p to project visible vertices and their embeddings onto a camera plane. Next, we interpolate features in the face mask region, fill in the background feature. Finally, add these features source image's DINOv2 features f to improve learning signals for LoRA.
In particular, we use GAGAvata (Chu and Harada, 2024) as generic model trained large-scale dataset using the general DINOv2 features in its reconstruction branch, which suitable to serve as our foundation model for fast adaptation. While DINOv2 features are robust for generic tasks, they may contain irrelevant information for avatar generation. Thus, adapting the layers of the generic avatar model focus relevant information within the DINOv2 feature space is necessary. We propose our Register Module for this purpose in the following sections.
3.2 LoRA for Fast Adaptation
To adapt to a particular identity, inspired by the literature in NLP, we use LoRA (Hu et al., 2022) for adaptation parameter-efficienn manner. For pre-trained weight matrix W Rmxx LoRA models the adapted weights W Wadapt by representing it as an addition of the pretrained weights W and an offset matrix AW, the latter of which is low-rank decomposable.
(1) where B E mR and A E IR"x and r < mmn n). During adaptation, only parameters A and B receive gradients. During inference, matrix, using equation 1. Thus, there is no change in inference time. For our purpose, we add LoRA weights A and B each parameter matrix in a pre-trained avatar model, except the DINOv2 model. In our implementation, for all experiments except ablations described in the supplementary material, we use the same rank 32 for all comparisons.
W Wddapt W + AW
+ BA,
merge the offset matrix AW with the pre-trained weight
3.3 Register Module
Figure illustrates the design of our Register Module. We hypothesize that, in addition to adding LoRA weights, we need mechanism for better extraction of fine details of identity, such as tattoos, wrinkles, muscular idiosyncrasies, and other personal features of an identity. To this end, we introduce a Register Module to improve the focus on adentity-specife details.
Feature Learning Procedure, Specifically, we propose to highlight detailed information in DINOv2 local features of the source image with the output of the Register Module. Let M = (V, E, F) be 3DMM mesh (I.i et al., 2017), where V is the of vertices, n(V) ) the number of vertices, E the set of edges and F is the set of facets. In our Register Module, we rig embeddings where D is the dimension of the embeddings, to vertices E V of mesh M. Given driving image camera pose and position posep, we compute the set of visible vertices U C V of the mesh M from
R=V))x
= visible )
(2)
Next, we project these points U to feature space in the camera plane S = [(i_j) E 2 sis H, < j < W} and their corresponding embeddings to a dense feature fs R lxWDD where H, W are the parameters of the image size, using Perspective Projection:
Us perspective_projecc((( pose K()).
5

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where (S) is the intrinsic camera matrix for a camera with S as the camera plane. The projected points are rounded off to the nearest integer. At points in the feature plane where a visible vertex E U is projected to, we assign the corresponding point's embedding from e. Hence the operation becomes:
fs [s(ug]= == =(u") for u' E U and u'g is the projection of u²
Given the set of points Ug on the camera plane, we compute an alpha shape (Edelsbrunner et al., 1983) to find the simple contour polygon Pu. of the vertex projections. Let interior(2 (P) represent all the points inside a simple polygon P. For each point p E interior(Pus) and & Us, we compute k nearest points in Us. and do inverse distance weighted interpolation for point p. Mathematically, interpolated feature ep for point p E interior( ( and & Us is defined as
Li=1 C
[s(p] = epp
(5)
Ei=1 1
where {u; i [1, v E Us} C Us is the set of k nearest projected vertices and d; |pp - v/ll2- For points p E and p & interior(Pu.) we assign an feature es. This results in a dense constructed feature fs "lxx>D from assigned features at each point in S. We further process these features using a CNN-based encoder Eproc-1.
fprec-1 1froc-1(fs).
(6)
where /prec-1 E RRHWWDDau,
We add these features to the source image's dense DINOv2 features färe E #R in order exploit identity-specifi information in fare dense and process the result with another CNN-base encoder Eproc- 2. Mathematically, the output of our Register Module freg is
freg 2proc-2 2S + fproc-1)
(7)
In our implementation, we use H = W = 296 and Dout = 256 as in Chu and Harada (2024). We set l = 11 and D 512 for all our comparisons.
Objective Functions. In order to make sure that the Register Module learns meaningful features, we constrain the training with two losses. First, we use the MSE loss between the driving image's DINOv2 features (fdri (f and output of the Register Module freg.
Ljeat = [|fdeise - -Sreyllž
(8)
Next, to ensure that the features learned in the Register Module are not similar to each other, regularize the embeddings e. This is enforced by
pcos(e) Lreg (VV)((VV-11)
where (X) = E. |XI,I,X (V) is the sum of the non-diagonal elements of a self- pairwise cosine distance. We use weighted combination of Leeat and Lyeg with weights X and Areg- Together, they form Lregister = Afeat + Aveg bre. In our implementation, we set Xveg 20 and feet 2.
X
Avatar Adaptation and Generation. To adapt to a particular identity, we pick the first frame of a video as the source image and select a random frame as the driving image. Next, we predict the Register Module's ' output freg from the source image's s DINOv2 features fieese and driving image's 3DMM parameters. Next, we pass the driving image's ss 3DMM parameters to the expression branch and Jreg to the reconstruction branch. The outputs of the corresponding branches are merged to produce coarse image. A neural renderer then produces the fine image.
During inference, we skip the Register Module and directly pass the source image's DINOv2 features fare to the reconstruction branch, while the rest of the process is the same as the training stage.
Experiments
4.1 Dataset Collection
We propose new dataset, namely RareFace-50. Prior work uses datasets with a large number of identities, e.g. VFHQ (Xie et al., 2022). However, these datasets mostly include videos of celebrities

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Source Image
Driving Image
I
ferc dense
Sproc-1
freg
Figure 4: Visualization learned features by the Register Module on our RareFace-50 dataset. We visualize 1) source image's DINOv2 feature fare denee 2) fproc-1. output from Eproc-1, and 3) freg- output of the Register Module. We compute the 2nd channel-wise PCA component and standardize the values. We observe that the Register Module improves the learning signals by highlighting face regions and dampening the background regions.
and well-known faces from television. Thus, they might lack in diversity in terms of age and high- frequency facial details, such wrinkles or unique tattoos (see Figure 4). These underrepresented human faces are difficult to faithfully generate by generic networks, such as GAGAvatar (Chu and Harada, 2024). Identifying this issue in existing datasets, we collect video dataset of 50 identities with unique facial details from YouTube. The dataset is collected from high-resolution close-up videos shot 1080p, 2K and 4K formats. resolution. The average duration of the videos in this dataset is around 15 seconds, with 2 videos per identity, resulting in the total number of videos in the dataset equal to 100. We intend to publish dataset for research purposes. In addition to RareFace-500 to evaluate our method. HDTF (Zhang et al., 2021) consists of 362 videos each cropped and resized to 512x512 resolution. VFHQ Test consists of 50 high quality videos from 50 different identities cropped and resized to 512 x 512 resolution. Each video around 4 to 10 seconds in diverse poses and settings.
detect faces, crop and resize face images to 512 x 512
VFHQ test set and HDTF dataset
We pre-process input videos using the tracking pipeline from Chu and Harada (2024). This step provides background-mater input frames, along with its tracked 3DMM parameters (these include view pose, eye pose, jaw pose, FLAME shape and expression parameters). We also pre-compute visible vertices of the 3DMM mesh fitted on particular frame. After this, we also compute the shppa-shape polygon for the projection of the visible vertices and the set of points that within this polygon given the scale of the projection screen size. See more details in the supplementary material.
4.2 Learned Features
In Figure 4, visualize the learned features of Register Module. Specifically, we visualize features 1) procc-1 from Fproc-1, and 2) freg from Fproc-2- We compute the 2nd channel-wise PCA component of DINOv2 features, and standardize and visualize using colormap between the range 3o, 3a for DINOv2 features and [-a, o for fyroc-1 and freg. Since the DINOv2 model trained in a self-supervised manner make features of different augmented views of an input image to be similar on diverse dataset, we observe features predicted by DINOv2 to have features irrelevant to the task hand, i.e., representing human faces. Moreover, the addition of the features from fproc-1 changes the characteristics of the added DINOv2 features (see the comparison of fare and freg in Figure 4), changing the distribution irrelevant regions of the DINOv2 features. In the supplementary material, we show additional visualization results, other PCA components, and an analysis on the norms of DINOv2 and Register Module features indicating that they improve meaningfully.
7

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Source Image
Driinglmage
Gaussian Noise
Learnabla Embeddings
Figure 5: Ablation results on the VFHQ Test dataset. We observe that our method performs better in preserving fine-details such as wrinkles and blemishes.
Table 1: Ablation study
proposed Register Module VFHQ Test dataset. In (a), present
the results with vanilla LoRA adaptation. In (b), we add noise to DINOv2 features during LoRA adaptation. In (c), we add learnable embeddings to DINOv2 features during LoRA adaptation. In (d), replace learnable embeddings rigged to vertices of the mesh with gaussian noise vectors. Best results are highlighted in bold.
Method
LPIPS
ACD
(a) LoRA
0.2666
0.3687
(b) LoRA Gaussian Noise
0.2699
0.3795
(c) LoRA Learnable Embeddings
0,2622
0.3604
(d) LoRA + Gaussian Noise Vertex Embeddings
0.2493
0,3623
Ours (LoRA + Register Module)
0.2470
0,3559
4.3 Ablation Study
We conduct an ablation study on our Register Module comparing our method with variants. This study shows the contribution of our proposed 3D feature space, which extends the idea of registers to 3D human faces. Specifically, variant (b) LoRA + Gaussian Noise, we sample gaussian noise
G (Gh, a) Hxx Dout, Wn, A((0, 1) and add this noise G to the features feesse during the LoRA adaptation stage. In variant (c) LoRA + Learnable Embeddings, we add a learnable embedding dictionary étearn HW D fare and make it trainable during LoRA adaptation. In variant (d) LoRA + Gaussian Noise Vertex Embeddings, we replace the learnable embeddings rigged to the vertices of the mesh with Gaussian noise our Register Module. In all variants, directly use dense during inference.
iid
fare
Figure shows visual results from these variants. Adding Gaussian Noise causes washed colors and overly smoothed details. Adding Learnable Embeddings improves the preservation of details and colors slightly. This variant would be the immediate extension of registers from Darcet et al. (2023) to our case. However, we notice that our proposed Register Module best preserves high-frequency details, such roughness of the face and fine wrinkles, by learning an appropriate 3D feature space for human faces. Table shows the corresponding quantitative results, demonstrating the efficacy of module in enhancing identity-specific details. We see that method has better perceptual similarity to the input image and better preserves identity. We encourage the readers to watch our supplementary video for additional results demonstrating the efficacy of our Register Module.
4.4 Evaluation
Qualitative Evaluation. Figure shows our qualitative results. Notice how our method faithfully reconstructs fine details and deentity-specifit features well compared to other methods. GAGAvatar

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Source Image
Driving
GAGAustar
LoRA
Meta Learning on LoRA
Our Method
Figure 6: Personalized head avatar generation VFHQ Test (row 1, and 3) and RareFace-50 (rows 4 and 5). We compare state-of-the-arr methods for adaptation (LoRA (Hu et al., 2022) and Meta-Learning (Zhang al., 2023a)). We observe that our method preserves fine details for identity- specific features and produces higher quality results as compared to other methods.
Table 2: Quantitative comparisons of approach with the baseline and other state-of-the-art adaptation methods. Results are highlighted as follows: Best and Second Best
HDTF
VFHQ Test
RareFace-50
Method
LPIPS
ACD|
LPIPS]
ACD1
LPIPS),
ACD1
GAGAvatar (Chu and Harada, 2024)
0.1747
0.3441
0.2540
0.3631
0.2953
0.4046
LoRA (Hu et al., 2022)
0.1770
0.3553
0.2666
0.3687
0,2855
0.3872
MetaPortrait (Zhang et al., 2023a)
0.1901
0.3559
0.2650
0.4131
0.2901
0.4504
Ours
0.1618
0.3156
0.2470
0.3559
0.2744
0.3792
frequently produces washed out colors (in rows and 5) and muted details (rows and 3). While LoRA performs better than GAGAvatar, still misses fine wrinkles and veins (rows 1, 2, and 3), LoRA
contrast in tattoos and skin (rows 4 and 5), and bumpy skin (row 3). Meta Learning weights produces artifacts on face (row 3) and in eyes (rows 1, 2, and 5). produces wrong expression compared to the driving image (all rows), misses tattoos on skin (row 4), and generates wrong colored lips. In general, our method learns to preserve high- high-frequency details in the source identity and produce higher quality results while using the same number of parameters as other methods. Baselines. We compare our method to baseline as the generic avatar generation model (Chu and Harada, 2024) and the state-of-the-art approaches, namely LoRA (Saunders and Namboodiri, 2024) and MetaPortrait (Zhang et al., 2023a) for adaptation in the cross-reconstruction setting. We use the same rank r = 32 for all our comparisons. Note that we implement the meta-learning algorithm from MetaPortrait (Zhang et al., 2023a) on LoRA weights for fair comparisons.

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Table 3: Additional comparisons with DoRA. In (a) and (b), we present results on LoRA and LoRA + Register Module (Ours) respectively. (c) and (d), we present results on DoRA (Liu et (2024)) and our Register Module with DoRA.
Method
LPIPS.
ACD4
(a) LoRA
0.2666
0,3687
(h) LoRA + Register Module
0.2470
0.3559
(c) DoRA
0.2685
0.3853
(d) DoRA Register Module
0.2668
0.3702
Evaluation Metries. To measure visual quality, we select challenging patches with high geg-frequency details from predicted frames and compare them against source image patches using Learned Per- ceptual Image Patch Similarity (LPIPS) (Zhang al., 2018) in the noss-reconstruccton setting. Furthermore, we estimate the identity preservation using the Average Content Distance (ACD) metric (Vougioukas et al. (2019)), by calculating the cosine distance between ArcFace (Deng et al., 2019) face recognition embeddings of synthesized and source images. Essentially, the idea is that the smaller the distance between those embeddings, the closer are the synthesized images to the input source images in terms of identity.
Quantitative Evaluation. Table 2 shows our quantitative results. Our method significantly out- performs the state-of-the-an offtthe-rtt in low-rank adaptation (LoRA and Meta Learning on LoRA) in terms of visual quality (LPIPS) and identity preservation (ACD). We encourage the readers watch our supplementary video for additional results demonstrating the efficacy of our Register Module.
4.5 Generalizability
We test the generalizability of our proposed Register Module other LoRA-like PEFT methods such as DoRA (Liu et al. (2024)) in Table 3. Unlike LoRA, DoRA decomposes the adapted weights W Wadapt into direction and magnitude components. Its equivalent of Eq. (1) is
W BA
W Wadept = m ||W + BAIl
(10)
where || Ill is the vector-wise norm of the matrix, along each column W gg is the pretrained weight matrix, E _ is a learnable direction vector initialized as ||]],. and B E gg and E "=""" are the learnable low-rank matrices with r < min(m, n).
We find that, while (c) DoRA performs worse than (a) LoRA in preservation of high- figh-frequency infor- mation and identity, introducing our register module during adaptation (d) improves its performance. This shows that our Register Module is compatible with various LoRA-like methods.
5 Conclusion
In conclusion, we introduce a novel method for personalized head avatar generation. State-of- the-art approaches for adaptation such as vanilla LoRA and meta-learning fail preserve high- frequency details and identity-specific features. We propose a novel Register Module that enhances the performance of LoRA, by teaching the layers attend to specific regions in the intermediate features of pre-trained model. To demonstrate the effectiveness of our method, we collect a dataset of talking individuals with distinctive facial features, such wrinkles and tattoos. Our method outperforms existing methods qualitatively and quantitatively, faithfully capturing unseen identities.
Limitations and Future Work. Although our Register Module successfully captures distinctive facial details, might produce suboptimal results for extreme side or back views that are rarely or all seen in video. In the future, plan extend our work faithfully animate avatars from such rare views of individuals.
A Acknowledgments This research was supported in part by NSF grant IIS-22 12046.
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References
Hangbo Bao, Li Dong. Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers. arXiv preprint arXiv:2106. 08254, 2021.
Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 2
Marcel C. Buehler, Gengyan Li, Erroll Wood, Leonhard Helminger, Xu Chen, Tanmay Shah. Daoye Wang. Stephan Garbin, Sergio Orts- rits-Esclaano, Oumar Hilliges. Dmitry Lagun, Jérémy Riviere, Paulo Gotardo, Thabo Beeler, Abhimitra Meka, and Kripasindhu Sarkar. Cafca: High-quality novel view synthesis of expressive faces from casual fho-shot captures. In SIGGRAPH Asia 2024 Conference Papers, New York, NY, USA, 2024. Association for Computing Machinery. 2
Zhixi Cai, Shreya Ghosh, Kalin Stefanov, Abhinav Dhall, Jianfei Cai, Hamid Rezatofighi, Reza Haffari, and Munawar Hayat. Marlin: Masked autoencoder for facial video representation learning. In CVPR, 2023. 34
Sai Tanmay Reddy Chakkera, Aggelina Chatziagapi, and Dimitris Samaras. Jean: Joint expression and audio- guided nerf-based talking face generation. arXiv preprint arXiv:2409. 12166, 2024.
Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-awand 3d generative adversarial networks. In Proceedings the IEEE/CVF conference computer vision and pattern recognition, 2022.
pages
Jonathan Chang and James Kelly. minlora: minimal pytorch library that allows you to apply lora to any pytorch model. attps://githhi com/ cccntu/ninLoRA Accessed: 2025-05-17. 175-17.11 24
Aggelina Chatziagapi, Grigorios G Chrysos, and Dimitris Samaras. Mi-nerf: Learning a single face nerf from multiple identities. arXiv preprint arXiv:2403. 19920 2024. 3
Yufan Chen, Lizhen Wang, Qijing Li. Hongjiang Xiao. Shengping Zhang, Hongxun Yao. and Yebin Liu. Monogaussianavatar: Monocular gaussian point-based head avatar. ACM SIGGRAPH 2024 Conference Papers, pages 1 1-9, 2024. 26
Kyusun Cho. Joungbin Lee, Heeji Yoon, Yeobin Hong, Jaehoon Ko, Sangjun Ahn, and Seungryong Kim. Gaussiantalker: Real-time talking head synthesis with 3d gaussian splatting. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 10985-10994, 2024. 1.3
Xuangeng Chu and Tatsuya Harada. Generalizable and animatable gaussian head avatar. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. 2, 3, 5, 6, 7, 9, 24, 26, 27, 29
Xuangeng Chu, Yu Li, Ailing Zeng, Tianyu Yang. Lijian Lin, Yunfei Liu, and Tatsuya Harada. Gpavatar: Generalizable and precise head avatar from image (s). arXiv preprint arXiv:2401.1022 10215, 2024. 3, 26, 29
Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski. Vision transformers need registers.
arXiv preprint arXiv:2309. 16588, 2023. 2, 8
Jiankang Deng. Jia Guo, Niannan Xue, and Stefanos Zafeiriou, Areface: Additive angular margin loss for deep
face recognition. CVPR, 2019. 10, 25
Yu Deng, Duomin Wang, Xiaohang Ren, Xingyu Chen, and Baoyuan Wang. Portrait4d: Learning one-shot 4d head avatar synthesis using synthetic data. In IEEE/CVF Conference Computer Vision and Pattern Recognition, 2024a. 26, 29
Yu Deng, Duomin Wang, and Baoyuan Wang. Portrait4d-v2: Pseudo multi-view data creates better 4d head synthesizer. arXiv preprint 13550 2024h. 26, 29
Jacob Devlin, Ming-Wor Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings the 2019 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, Volume I (Long and Minncapolis, Minnesota, 2019, Association for Computational Linguisties.
Short Papers), pages
Helisa Dhamo, Yinyu Nie, Arthur Moreau, Jifei Song, Richard Shaw, Yiren Zhou, and Eduardo Pérez- Fellz-eel. Headgas: Real-tin animatable head avatars via 3d gaussian splatting. In European Conference on Computer Vision, pages 459 476. Springer, 2024. 3
11

---

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenbom. Xiaohua Zhai, Thomas Unterthiner. Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image worth 16x words: Transformers for image recognition scale. In International Conference on Learning Representations, 2021. 4
Hao-Bin Duan, Miao Wang, Jin-Chuan Shi, Xu-Chuan Chen, and Yan-Pei Cao. Bakedavatar: Baking neural fields for real-time head avatar synthesis. ACM Trans. Graph., 42(6), 2023. 3
H. Edelsbrunner, D. Kirkpatrick, and R. Scidel. On the shape of set of points in the plane. IEEE Transactions on Information Theory, 2944)555 1983.
Guy Gafni, Justus Thies, Michael Zollhöfer, and Matthias Nießner. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction, 2020. 1,
Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. Portrait neural radiance fields from a single image. arXiv preprint arXiv:2012 2.05903, 2020. 3
Pablo Garrido, Levi Valgaerts, Ole Rehmsen. Thorsten Thormahlen, Patrick Perez, and Christian Theobalt. Automatic face reenactment. In Proceedings of the IEEE conference computer vision and pattern recognition. pages 4217 4224, 2014. 1,2
Pablo Garrido, Levi Valgaerts, Hamid Sarmadi, Ingmar Steiner, Kiran Varanasi, Patrick Perez, and Christian Theobalt. Vdub: Modifying face video of actors for plausible visual alignment to a dubbed audio track. In Computer gruphics forum, pages 193-204. Wiley Online Library, 2015. 1,2
Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference Artificial Intelligence and Statistics, pages 249 256, Chia Laguna Resort, Sardinia, Italy, 2010. PMLR. 24
Jia Guo, Jiankang Deng, Xiang An, Jack Yu, and Baris Gecer. Insightface repository model zoo.
Michelle Guo, Matt Jen- Yuan Chiang, Igor Santesteban, Nikolaos Sarafianos, Hsiao-yu Chen, Oshri Halimi, Aljaž Božič, Shunsuke Saito, Jiajun Wu, C. Karen Liu, Tuur Stuyck, and Egor Larionov. Pge: Physics-bat based gaussian cloth from single pose. In Proceedings of the IEEÉ/CVF Conference on Computer Vision and Puttern Recognition (CVPR), pages 21215-21225, 2025. 30
Yisheng He, Xiandong Gu, Xiaodan Ye, Chao Xu, Zhengyi Zhao, Yuan Dong, Weihan Yuan, Zilong Dong, and Liefeng Bo. Lam: Large avatar model for one-shot animatable gaussian head. Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers, pages 1-13, 2025. 27
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 18):1735-1780 1997. 3
Neil Houlsby, Andrei Giurgiu. Stanislaw Jastrzebski. Bruna Morrone, Quentin De Laroussilhe, Andrea Ges- mundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for nlp. In International conference on machine learming, pages 2790-2799. PMLR, 2019. 3
Edward Hu, Yclong Shen, Phillip Wallis, Zeyuan Allen Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. 2, 3, 4. 5, 9, 24, 27
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühllrr. and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4), 2023. 1, 3
Hyeongwoo Kim, Pablo Garrido, Ayush Tewari, Weipeng Xu, Justus Thies, Matthias Niessner, Patrick Pérez, Christian Richardt, Michael Zollhöfer, and Christian Theobalt. Deep video portraits. ACM Transactions on Graphics (TOG), 37(4):1 14, 2018. 1, 2
Diederik P. Kingma and Jimmy Ba. Adam: method for stochastic optimization, 2017. 24
Tobias Kirschstein, Simon Giebenhain, and Matthias NieBner. Diffusionavatars: Deferred diffusion for high- fidelity 3d head avatars. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattrn Recognition, pages 5481 1481-5 2024a.
Tobias Kirschstein, Simon Giebenhain, Jiapeng Tang, Markos Georgopoulos. and Matthias NieBner. Gghead: Fast and generalizable 3d gaussian heads. In SIGGRAPH Asia 2024 Conference Popers, pages 1-11, 2024b.
12

---

Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Jun Zhou, and Lin Gu. Instag: Learning personalized 3d talking head from lew-second video. arXiv preprini arXiv:2502.20387, 2025.
Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning model of facial shape and expression from 4D scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 2017. 5,
Shih-Yang Yang Liu, Chien-Yi Wang. Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang. Kwang-Ting Cheng, and Min- Min-Hung Chen. Dora: Weight-dccompost low-rank adaptation. arXiv preprint vrXiv:2402.09355 2024. 10
Yiqun Mci, Yu Zeng, He Zhang, Zhixin Shu, Xuaner Zhang, Sai Bi, Jianming Zhang, Hyunloon Jung, and Vishal M Patel. Holo-relighting: Controllable volumetric portrait relighting from single image. arXiv preprint arXiv:2403.096. 2024. 30
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. ECCV, 2020. 2,
Alex Nichol and John Schulman. Reptile: a scalable metalcarning algorithm. arXiv preprint arXiv: 183.09803..2299 2(3):4, 2018. 25
Jim Nilsson and Tomas Akenine Möller. Understanding ssim, 2020. 25
Yotam Nitzan, Kfir Aberman, Qiurui He, Orly Liba, Michal Yarom, Yossi Gandelsman, Inbar Mosseri, Yael Pritch, and Daniel Cohen-Or. Mystyle: A personalized generative prior. arXiv preprint arXiv:2203.17272. 2022. 2,3 3
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07/990 2023. 2
Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, and Ricardo Martin-Brualla Nerfies: Deformable neural radiance fields. ICCV, 2021a. 1,3
Keunhong Park, Utkarsh Sinha. Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-Brushe and Steven M Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. arXiv preprint arxiv:2 13228, 2021b. 1,3
Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C.V. Jawahar. A lip sync expert all you need for speech to lip generation in the wild. In Proceedings of the 28th ACM International Conference on Multimedia, page 484 492, 2020. 1,2
Albert Pumarola, Antonio Agudo, Aleix M Martinez, Alherto Sanfeliu, and Francese Moreno-Noguer. Ganima- tion: One-shot anatomically consistent facial animation. International Journal of Computer Vision, 128(3): 698-713, 2020. 1,2
Albert Pumarola, Enric Corona, Gerard Pons- Moll, and Francese Moreno- Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10318-10327, 2021. 1,3
Luchao Qi, Jiaye Wu, Annie Wang, Shengze Wang, and Roni Sengupta. My3dgen: A scalable personalized 3d generative model. In 2025 JEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 961-972. IEEE, 2025.
Shenhan Qian, Tohias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Gicbenhain, and Matthias Nießner. Gruusiannvvtaar:: Photorealistic head avatars with rigged 3d gaussians. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pages 20299-20309, 2024. 1,3
Pramod Rao, Gereon Fox, Abhimitra Meka, Mallikarjun B R, Fangneng Zhan, Tim Weyrich, Bernd Bickel, Hanspeter Plister, Wojeiech Matusik. Mohamed Elgharib, and Christian Theobalt. Lite2relight: 3w-ae single image portrait relighting. In ACM SIGGRAPH 2024 Conference Pupers, New York, NY, USA, 2024. Association for Computing Machinery. 30
Tal Reiss. Bar Cavia. and Yedid Hoshen. Detecting deepfakes without seeing any D.Xi::3311.01453 2023. 34
arXiv preprint
Daniel Roich, Ron Mokady, Amit H. Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Trans. Graph., 42(1):6:116:1) 6:11, 2023. 2
13

---

Jack Saunders and Vinay Namboodiri. Talklora: Low-rank adaptation for speech-driven animation. arXiv preprint arXiv:2408. 2024. 3,
Zhijing Shao, Zhaolong Wang. Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang, Mingming Fan, and Zeyu Wang. SplattiggAvatar Realistic Real-Time Human Avatars with Beehh-Emeedded Gaussian Splatting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 26
Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First order motion model for image animation. In Conference Neural Information Processing Systems (NeurIPS), 2019. 2
Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al. End-to-end memory networks. Advances in neural information processing systems, 28, 2015. 3
Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, and Ren Ng. Learned initializations for optimizing coordinate-bass neural representations. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2845-2888 2854, 2021. 2
Justus Thies, Michael Zollhofer, Mare Stamminger, Christian Theobalt, and Matthias Nießner. Face2face: Real-time face capture and reenactment of rgb videos. In Proceedings of the IEEE conference computer
vision and patter recognition, pages 2387-23955 2016. 1, 2
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Lukasz Kaiser. and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 4
Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. End-to-end speech-driven realistic facial animation with temporal gans. In CVPRW, 2019. 10
Konstantinos Vougioukas,, Stavros Petridis, and Maja Pantic. Realistic speech-driven facial animation with gans. International Journal of Computer Vision, 12865):338--1413 2020. 1,2
Jie Wang, Jiu- Ju-Cheng Xie, Xianyan Li, Feng Xu, Chi-Man Pun, and Hao Gao. Gaussianhead: High-fidelity head avatars with lcarnable gaussian derivation. IEEE Transactions on Visualization and Computer Graphics, 2025. 3
Jason Weston, Sumit Chopra, and Antoine Bordes. Memory networks. arXiv preprint arXiv:1410.3916. 2014.
Jun Xiang. Xuan Gao, Yudong Guo, and Juyong Zhang, Flashavatar: High-fidelity head avatar with efficient gaussian embedding. The IEEE Conference on Computer Vision and Puttern Recognition (CVPR), 2024.
26
Lianginn Xie, Xintao Wang, Honglun Zhang, Chao Dong, and Ying Shan. Vfhq: A high-quality dataset and benchmark for video face super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patiern Recognition, pages 657-666. 2022. 2, 6
Jingyi Xu, Hieu Le, Zhixin Shu, Yang Wang, Yi-Hsuan Tsai, and Dimitris Samaras. Learning frame- wise emotion intensity for audio-driven talking-head generation. arXiv preprint 4rXiv:2409.19500 2024a.
Sicheng Xu, Guojun Chen, Yu-Xiao Guo, Jiaolong Yang, Chong Li, Zhenyu Zang. Yizhong Zhang, Xin Tong, and Baining Guo. Vasa-1: Lifelike audio-driven talking faces generated in real time. arXiv preprint 2404. 10667, 2024b. 2
Yuclang Xu, Benwang Chen, Zhe Li, Hongwen Zhang, Lizhen Wang, Zerong Zheng, and Yebin Liu. Gaussian head avatar: Ultra high-fidelity head avatar via dynamic gaussians. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), 2024c. 1.3
Zhixuan Yu, Ziqian Bai, Abhimitra Meka, Feitong Tan, Qiangeng Xu, Rohit Pandey, Sean Fanello, Hyun Soo Park, and Yinda Zhang. One2avatar: Generative implicit head avatar for few-shot user adaptation. arXiv preprint arXiv:2402. 1/ 1909, 2024. 3
Bowen Zhang, Chenyang Qi, Pan Zhang, Bo Zhang, Hsiang'Tao Wu, Dong Chen, Qifeng Chen, Yong Wang, and Fang Wen. Metaportrait: preetervpp talking head generation with fast personalized adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22056-22105,
2023a. 2, 3, 9,
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3836-3847, 2023b. 3
14

---

Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features perceptual metric. In CVPR, 2018. 10, 25
Zhimeng Zhang, Lincheng Li, Yu Ding, and Changjie Fan. Flow-guided one-shot talking face generation with high-resolution audio-visus dataset. In Proceedings of the IEEE/CVF Conference Computer Vision and Puttern Recognition, pages 3661-3670, 2021.
Hang Zhou, Yasheng Sun, Wayne Wu, Chen Change Loy, Xiaogang Wang, and Ziwei Liu. Pose-controllable talking face generation by implicitly modularized audio-visual representation. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2
Zheng Zhu, Guan Huang, Jiankang Deng. Yun Junjie Huang. Xinze Chen, Jiagang Zhu, Tian Yang. Jiwen Lu, Dalong Du, and Jie Zhou. Webface260m: A benchmark unveiling the power of million-scale deep face recognition. In CVPR, 2021. 25
Wojciech Zielonka, Timo Bolkart, and Justus Thies. Instant volumetric head avatars. In Proceedings of the JEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4574 4584, 2023.
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Appendix
The appendix is organized as follows:
Additional Ablation Study in Sec. A
2. Implementation Details in Sec. B
3. Dataset Collection Details in Sec. C
4. Additional Results in Sec. D
5. User Study Details in Sec. E
6. Discussion: Broader Impact, Limitations, and Ethical Considerations in Sec. F
We strongly encourage the readers to watch our supplementary video.
A Additional Ablation Study
Table 4: Ablation study on losses to adapt with method. In (a), remove feat during adaptation. In (b), we remove Lreg during adaptation.
Method
LPIPS
ACD
(a) Ours w/o Iefeat
0.2574
0.3604
(b) Ours w/o Tree
0,2508
0.3574
Ours
0.2470
0.3559
Table 5: Ablation study on the effect of Areg. the weight of Lyeg to adapt with our method on the VFHQTest dataset.
Areg
LPIPS
ACDJ
1
0.2520
0.3616
8
0.2502
0.3606
16
0,2485
0.3594
0.2470
0.3559
32
0.2500
0.3663
Table Ablation study on length of videos to adapt the head avatar. We set the adaptation video length 4, 2. and seconds.
Method
LPIPS]
ACDJ
(a) Ours w/ 4 see
0.2471
0.3639
(b) Ours w/ 2 sec
0.2471
0.3656
(c) Ours w/ sec
0.2477
0.3687
We conduct additional ablation studies our proposed method. Specifically, we ablate the various losses we propose. Table 4(a), we remove the loss L to supervise the output of our register module during adaptation, and in (b) we remove Lreg to make the learned embeddings in our Register Module different from each other. We see that removal of these losses causes a drop in performance compared when both losses are present. We also ablate the hyperparameter Åreg. the weight of Lyeg. in Table 5. We find that Areg = 20 gives the best results. Further, we ablate on the length of the videos used to adapt head avatars in Table 6. We see that reducing the length of the adaptation video causes drop in the performance of the method. Note that we trim the original videos to the first 4, and second for these experiments.
For completeness, we also tried the case when our Register Module is used during inference; however it harms the output quality we increase in the ACD metric by around 5%.
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B Implementation Details
B.1 Addition of LoRA to layers
We use minLoRA (Chang and Kelly) library to add LoRA (Hu et al., 2022) parameters to all layers of a pretrained pytorch model. During training LoRA instantialized as separate parameters B E _mxr, A E grx" and r < min(m, n) from the pretrained parameters W E = of the module, so that these parameters can be trained. During inference the LoRA parameters are merged with the pretrained parameters and assigned as the new pretrained parameters according to:
Wadapt == pre + AW Wrr + BA.
(11)
This ensures that the LoRA layers add no overhead to the pipeline during the inference. Given pretrained GAGAvatar model, we add LoRA weights to all layers except in the DINOv2 feature
extractor.
B.2 Dataset Preprocessing
Given the expression code, shape code, and camera pose predicted during 3DMM fitting, we predict a 3DMM mesh. We compute visible vertices of 3DMM mesh from the computed camera pose of a particular frame using trimesh's 's RayMessnntersector implementation. Specifically, we cast rays from the camera origin to each point in the mesh and compute whether any line intersects the mesh (if an intersection exists, the point not visible). Given these visible points in 3D space, we find a screen space camera projection on screen of the same size as DINOv2 feature space (H = W = 296) using Perspective Projection. Then we find an alpha-shape of these projected points with a = 0.065.
Next, we compute all the points in the alpha-shape polygon using parallelized point-in-polygon test. For all points in the polygon that are not projected points, we also compute the nearest projected points and distances from those projected points, where k 11.
B.3 Register Module Details
The embeddings rigged to vertices on the 3DMM mesh (Li et al., 2017) are modeled in a 3D space in our register module, However, these points are projected onto a 2D space using a camera projection following which, we interpolate the features using a weighted sum of the k nearest neighbors to fill up the face region in densely constructed feature Js. Using the entire set of the vertices would cause all points to be projected onto the densely constructed feature ss, thus impacting the interpolation process (projection of points from the back of the head might be in the k nearest neighbors of point p E interior (Pus)). Thus, we only project visible points from any given view in our register module.
We model Eproc-1 as a convolutional module, with 4 convolutional layers of channel sizes [512, 512, 256, 256] and kernel sizes set to 3 for each layer. Eproc-2 is also a convolutional module with layers of channel sizes set to 256. The first layers have a kernel size of while the last layer has a kernel size of 1.
B.4
Training Details
B.4.1
Our Method
We initialize embeddings using Xavier Normal initialization (Glorot and Bengio, 2010). We adapt head avatars with our method for a total of 1000 iterations. The batch size is set to 2. We use Adam (Kingma and Ba, 2017) optimizer with learning rate set to 1e - for the LoRA layers whereas the learning rate is set to 1e 3 for parameters in the Register Module. We use a linear learning rate scheduler with a start factor of 1.0 and an end factor of 0. at the 1000th iteration. Along with our proposed losses, we also keep the losses proposed by GAGAvatar (Chu and Harada (2024)), namely, RGB losses between predicted image and driving image, a perceptual loss between the predicted images and driver image, and Liiftings loss between predicted points from reconstruction branch and vertices of the 3DMM mesh fitted on the driving image. Our adaptation takes & 35 minutes on an RTX A5000 GPU, consuming 2 23GB of VRAM. During inference, we load and merge the LoRA weights into their corresponding layer parameters. Thus, there is no overhead during inference, i.e., consumes the same amount of resources GAGAvatar during inference.
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B.4.2 Baselines and A Ablations
For all comparisons with the vanilla LoRA, we use the same hyperparameters as our method. That is, we adapt with vanilla LoRA for a total of 1000 iterations, set the batch size to 2, use Adam optimizer with learning rate set to le - 4 for the LoRA layers, and use linear learning rate scheduler with start factor of 1.0 and an end factor of 0.1 at the 1000th iteration. This adaptation takes Ra 25 minutes on an RTX A5000 GPU, consuming = 14.9GB of VRAM.
Following MetaPortrait (Zhang al., 2023a), we implement Reptile (Nichol and Schulman (2018)), a MAML based strategy for meta-learning in our low-rank adaptation task. We perform pre-training on our RareFace-50 dataset. Following the formulation of MetaPortrait, we formulate the task of adapting to a particular identity as an inner loop task. Thus, we adapt to a randomly sampled identity at each outer step. For all comparisons with Meta-Learnngg on LoRA, we set rank r = 32, the inner loop learning rate to 2e - 4, and the outer update step size to 2e 5. The number of inner loop steps are set to 120, and number of elements batch inner loop set to 4. We set the number of outer iterations to 4800. Given resource constraints, we implement a single GPU version of Reptile (Nichol and Schulman, 2018), thus taking 12 days to complete the pretraining task on a Quadro RTX 8000 GPU, consuming z 45GB of VRAM. After the pre-training task, we adapt the model an identity for 120 steps with the same learning rate as the inner loop, which takes 4 minutes on an RTX A5000 GPU consuming R 14.9GB of VRAM. We then use these adapted weights for inference by merging these LoRA weights to the corresponding layers.
For all experiments with learnable parameters Elearn as a replacement to our Register Module, we set the learning rate for Elearn parameters be 1e-3. 3.
B.5 Metrics
We compute the visual quality metries namely, LPIPS (Zhang et al. (2018)), specific challenging crops with high frequency details from predicted frames and compare them against source image patches. The identity preservation metric (ACD) is measured using ArcFace (Deng et al. (2019)), Res network trained on WebFace (Zhu et al. (2021)). Specifically, we used "buffalo_I" model from the insightface repository (Guo al.).
We compute the image metrics between source image and predicted image on challenging high- frequency feature crops, that include unique facial details of individuals and are the focus of our research. Since there might be a slight misalignment between the generated and ground truth head poses (because of the generation), to be unreliable. Specifically, PSNR captures blur, but doesn't 't capture is our main goal), as it still operates a pixel-by-pixed basis, as in,
find that pixel-based (PSNR) or locality- based (SSIM) metrics
details (which
MSE 1 mm 1=
M-
ΣΣ
(x|i.)]- vli PSNR 10log10 MSE'
255
where X and y are input images. Moreover, as indicated in the illustration below, and Figure 2 of Understanding SSIM (Nilsson and Akenine-Möller (2020)), we see that while perceptually indistinguishable on high-resolution screens, SSIM fails to recognize the perceptual similarity between the images. Note that below, we illustrate a 128x 128 image using a 4x4 grid. However, the actual SSIM computation was done using the entire 128x 128 images (this is only to illustrate the weaknesses of SSIM for perceptually similar images, using a toy example).
128
128
128
128
0
255
0
255
128
128
128
128
255
0
255
0
SSIM
128
128
128
128
0
255
0
255
L L.C.S S 0.0036
128
128
128
128
255
0
255
0
where L = 1, C 0.0036, S
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255
0
255
255
0
255
0
255
0
255
0
0
255
0
255
SSIM
0
255
0
255
255
0
255
0
L. U.C.S= -0.9964
255
0
255
0
0
255
0
255
1
where L 1, C = S -0.9964
Therefore, we rely on LPIPS, as it focuses perceptual differences between images; the primary task of our research.
C Data Collection
We collect data from Youtube of people knowingly appearing in interviews in public broadeasts with distinctive facial details, such as wrinkles or tattoos. These characteristics are under-represented in existing datasets. We will present the dataset as set of links, along with trim times and crop position coordinates. Additionally, this dataset will be maintained using an automatic script that checks and removes links from the list that no longer exist in YouTube.
D Additional Results
D.1 Additional Comparisons
In this section, present results additional baseline. Specifically, compare method with single-identity models, specifically SplattingAvataar (Shao et al. (2024)).
Table 7: Additional comparisons with Single Identity models (e.g. SplattingAvatar (Shao al. (2024))) on VFHQTest. In (a) and (b) we present results on LoRA and LoRA + Register Module (Ours) respectively. In (c) we present the results for AplattingAvatar (Shao et al. (2024)).
Method
LPIPS
ACD1
(a) LoRA
0.2666
0.3687
(b) LoRA Register Module (Ours)
0.2470
0.3559
(c) SplattingAvatar (Shao et al. (2024))
0.4573
0,5238
We also present results for a suggested method (Splatting Avatar (Shao et al. (2024))) on VFHQ Test Set Table 7. SplattingAvatar (Shao et al. (2024)) produces very poor performance quantitatively. We see similar trend in our qualitative observations. In general, the head avatars produced by head reconstruction methods (such as SplattingAvatar (Shao al. (2024)), FlashAvatar (Xiang et al. (Chen et al. (2024)), etc.) overfit to the training set (identity and
(2024)), or MonoGaussian expressions). Inferring them using different expressions as input (not seen during training) leads to poor performance.
Here, we also create distinction between single-identity head avatar models (such as Splatting (Shao et al. (2024)), Avatar (Xiang et al. (2024)), or MonoGaussianAvataa (Chen et al. (2024)), etc.), generic head avatar models (such GAGAvatar (Chu and Harada (2024)), GPAvatar (Chu et al. (2024)), Portrait4D (Deng et al. (2024a.b)), etc.), and gemeric-personnlizze models (our task in this paper): 1) Single-identity head avatar models require from-scratch training for each identity. Since they are limited to data from single identity, they are unable to learn a generalizable representation and often fail in
cases. Further, limiting to a single identity also limits the ability to scale the dataset size. 2) Generic head avatar models, on the other hand, are trained on large multi-identity diverse datasets. Thus, they can usually generalize to novel inputs (new identity and unseen expressions). These methods often trade-off generalizability with identity specificity, and might create "average" features when faced with -fiit--distiitttinn samples. 3) Thus, personalization of generic head avatar models is necessary to capture identity-specific details. Our method personalizes a generic head avatar model to produce distinctive, high-frequency features that generic head avatar models "average" out.
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Further, we would like to distinguish our method from LAM (He et al. (2025)) is one-shot head avatar model that produces an animatable Gaussian head avatar from a single image. Our goal in this paper is to personalize a generalized head-avatar model to particular identity, such that high- frequency details are preserved. L.M focuses on animating single image, while we personalize single image animation model using short clip. Moreover, our method is different from LAM's, which rigs embeddings to the vertices 3DMM's mesh with cross-attention layers to learn from DINOv2 features. In contrast, Register Module operates in learnable 3D feature space and projects back to the 2D space for facilitating LoRA adaptation, and is only used during training.
D.2 Details of Adaptation
Adaptation Duration. In this section, we discuss the adaptation durations of our baselines and our method. Vanilla LoRA and our method take = minutes and 35 minutes to adapt respectively on an RTX A5000 GPU. Whereas, meta-learning
LoRA requires much longer 12 day period on an RTX Quadro 8000 GPU for the pretraining objective, after which requires 4 minutes on RTX A5000 GPU. However, during inference, all of these methods have the same inference times as GAGAvatar (Chu and Harada (2024)).
Adaptation Parameters. In this section, we discuss the number of parameters during adaptation and inference of our baselines and our method. During adaptation, we introduce 4. 4.7M parameters as LoRA weights to the pretrained layers in all baselines. Our Register Module adds another 18. 5M 11% of the total
parameters. Thus, our method has 23.2M parameters during adaptation, which number of parameters (199M parameters) in GAGAvatar. During adaptation, we discard the trained Register Module, which lends to the same efficiency as GAGAvatar and other baselines.
D.3
User Study
User Preference Identity Preservation
User Preference Visual Quality
LORA
10.1%
21.8%
78.2%
Our Method
Our Method
Method
Our Method
Figure 7: User Study. Preference (%) in terms of identity preservation, and visual quality, comparing LoRA (Hu al., 2022), and our method.
conduct user study to qualitatively and subjectively compare our method against LoRA (see Sec. E for details). The results of user study are shown in Fig. 7. We find that 78.2% of the users prefer our method as compared to LoRA in terms of identity preservation. Furthermore, we find that 89.9% of the users prefer our results compared to LoRA in terms of visual quality.
D.4 Additional Qualitative Results
In Fig. 8, we show that, compared to LoRA, our method effectively captures high-frequency details like wrinkles. We show additional results of our method against the baselines on VFHQ Test in Fig. and on RareFace-50 in Fig. 10. Fig 11 and 12 show visualizations of feature norms along the channel dimensions for two identities from RareFaces-50 The values are visualized using colormaps between
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LoRA
Our Method
Figure 8: We show facial details that are well captured by our method with Register Module, such as wrinkles and skin folds are realistic and have higher quality than vanilla LoRA. Please note the enlarged insets of specific details.
[-3a, 30] for fiense and frey and [-o, oj for fproc-1- We visualize the first four channel-wise PCA components in Fig. 13 to 18. We observe that the Register Module improves the learning signals by highlighting face regions and dampening the background regions.
E User Study Details
As mentioned in Sec. D.3, qualitatively compare our method with vanilla LoRA adaptation method with a user study. We describe the details of the user study here. Fig. 19 shows the interface that we use for this user study. A total of 18 users responded our user study. We generate head avatars given source videos from VFHQ Test and RareFace-50 using our method and LoRA. The outputs are placed side by side and the left-right orders are assigned randomly the users are unaware of which method is ours. Each generated video is z 5 to 10 seconds long, concantenated with the source identity image and the driving video. Users are asked two questions: "Which method's s avatar best looks like the source image identity?" and "Which method avatar has better visual quality?" The users can choose as answer "Method A" or "Method B" or both. Label "Method A" is placed to the left of label "Method B" and the generated videos are randomly placed in terms of left-right order. The answers are collected through google form. The videos are attached to the google form using link to google drive, and the users encouraged to download the vidcos
make sure that
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Source Image
Driving Image
GAGAvatar
LoRA
Meta Learning on LoRA Our Method
Source Image
Driving Image
GAGAvatar
LoRA
Meta Learning on LoRA Our Method
Figure 9: Additional results of personalized head avatar generation on VFHQ Test. Please zoom in for better details.
to view them on their system. This is done to make sure that differences in high resolution are evident the users.
Discussion
F1 Limitations
Our work is in line with gechting-agnostie methods that do not explicitly model lighting (e.g.. GAGAvatar (Chu and Harada (2024)), GPAvatar (Chu al. (2024)), Portrait4D (Deng et (2024a,b)), etc.) to model avatars. Therefore, lighting effects from the source image, such highlights and shadows, are prone to being "baked" into the model's 's texture map implicit appearance representation. While modeling light for avatar relighting is interesting, is separate line of work
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Source Image
Driving Image
GAGAvatar
LoRA
Meta Learning on LoRA Method
Source Image
Driving Image
GAGAvatar
LoRA
Meta Learning LoRA
Our Method
Figure 10:
Additional results of personalized head avatar generation RareFace-50 dataset. Please zoom in for better details.
(e.g., Portrait Video Relighting (Rao et al. (2024)), Holo-Relighting (Mei et al. (2024))). We leave the task of incorporating lighting effects for the future.
Our approach does not explicitly address cloth modeling. Hence, may not preserve clothing details. While modeling cloth characteristies and dynamics is interesting, it is separate line of work (c.g. Guo et al. (2025)). We leave the integration of cloth modeling techniques into our approach as an interesting direction for future research.
An important factor of our method the 3DMM fitting that is used extract the head pose, camera parameters, and 3DMM mesh parameters (see Sec. 3.3 of the main paper). This fitting can be noisy and the error can be propagated to final generated videos. Improving the face tracking further would be an interesting future work. Further, 3DMM fitting does not model asymmetric/extreem expressions (such as winking) and the movement of the tongue, which is another interesting line of work to pursue.
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Source Image
Driving Image
fdce
fproc-1
freg
Source Image
Driving Image
deess
f
freg
Figure 11: Visualization of learned features by the Register Module on our RareFace-50 dataset. We visualize the 1) source image's 's DINOv2 feature farese- 2) Sprac-1, output from Fprrae11 and 3) freg- output of the Register Module. We compute norms of the features along the embedding dimensions and standardize values.
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Source Image
Driving Image
I dense
Sproc-1
freg
Source Image
Driving Image
dense
p
freg
Figure 12: Visualization of learned features by the Register Module on our RareFace-50 dataset. We
visualize the source image's DINOv2 feature pare denses 2) fproc-1. output from Eproc-1- and 3) freg-
output of the Register Module. We compute norms of the features along the embedding dimensions and standardize values.
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Source Image
Driving Image
J
densee
Sproc-1
freg
Figure 13: Visualization of learned features by the Register Module on our RareFace-50 dataset. We compute the 1st channel-we -wice PCA component and standardize the values.
Source Image
Driving Image
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Figure 14: Visualization of learned features by the Register Module on our RareFace-50 dataset. We compute the 2nd channel-wise PCA component and standardize the values.
Our current implementation projects visible vertices and performs interpolation rather than using rasterization- based approach. this pipeline is not employed during inference, it does not impact the speed or scalability of the method. While we do not expect this design choice to have significant effect on our results, incorporating rasterization- based implementation could offer an alternative formulation, which we plan to explore in future work.
In this work, goal is to personalize a pre-trained generic model, GAGAvatar, thus inheriting architectural choices from the original model. As these models produce videos in a frame-by-frand manner, they apply an exponential moving average (EMA) filter on expression and pose along the time axis in order to reduce temporal inconsistencies. Using their exponential smoothing, we did observe any noticeable temporal inconsistencies our outputs. However, temporal consistency is crucial in real-world settings and can be further explored as future work, developing more fundamental solutions. Since 3D head avatar generation methods produce videos in a frame-by-frame manner, using tracked 3DMM parameters, there are two broad directions that can be pursued to make sure temporal consistency is fundamentally "baked-in": (a) make 3DMM parameter tracking temporally consistent (e.g., predict 3DMM parameters for window of frames instead of one-by-one [1], etc.) and train and infer the head avatar generation model on these tracked parameters, (b) make 3D head avatar generation temporally consistent (c.g., predict window of future frames given
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Figure 15: Visualization learned features by the Register Module on our RareFace-50 dataset. We compute the 3rd channel-wise PCA component and standardize the values.
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Figure 16: Visualization of learned features by the Register Module on our RareFace-50 dataset. We compute the 1st channel-wise PCA component and standardize the values.
a history of frames a sliding window fashion [2], and/or enable temporal consistency losses using landmark tracking [3], include other transformer-based modules etc.).
F22 Ethical Considerations and Broader Impacts
While our method has significant promise across diverse applications, it also carries the risk of abuse - for example, in creating "deep fakes". These can be used by users with malicious intent to spread misinformation. To prevent this, it is imperative to develop forensic tools to detect fake videos Cai et al. (2023); Reiss et al. (2023). We intend to share improve this research, which we will release them with strict licenses that only allow usage for academic research. When used ethically and responsibly, our method can offer profound benefits across industries - from video conferencing to the entertainment sector. In addition, we have also put appropriate procedures (see Sec. C) to fair and safe use of videos from the dataset collect.
code, dataset and models to
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Figure 17: Visualization of learned features by the Register Module on our RareFace-50 dataset. We compute the 2nd channel-wise PCA component and standardize the values.
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Figure 18: Visualization of learned features by the Register Module on our RareFace-50 dataset. We compute the 3rd channel-wise PCA component and standardize the values.
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User Study for Head Avatar Personalization
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Figure 19: User Study Interface. We ask each watch 8 videos and answer which method preserves the source image identity and which method has the best visual quality.
36


Reviews:
[{"review_id": "7GdCIzCO5g", "reviewer": "Reviewer_xCbz", "summary": "This manuscript proposes a personalized head modeling approach for the domain of personalized avatar modeling, combining low-rank adaptation (LoRA) with a three-dimensional register module. The method aims to enhance the modeling of high-frequency, personalized facial details with minimal parameter updates, effectively addressing the limitations of traditional general-purpose models and existing efficient fine-tuning methods in detail preservation.", "questions": "1. The regularization loss $L_{reg}$ in Equation (9) imposes a similarity constraint across all vertex embeddings. While this is effective for preventing overfitting, it may also lead to over-smoothing of features, thereby suppressing the inherent distinctiveness of local regions such as the lips and eyes. The weight for this loss, $\\lambda_{reg}$, is set to 20, which is significantly higher than the weight of 2 for the feature alignment loss. Could the authors provide a justification for this choice? I recommend adding an ablation study on the selection of the hyperparameter $\\lambda_{reg}$.\n2. Following up on question 1, what specific information do the embeddings corresponding to each vertex actually learn? Are there any qualitative conclusions that can be drawn about the learned representations?\n3. Could the authors clarify the rationale for summing the `source` features with the features that have been projected by the Register Module? It seems these two sets of features are not aligned to the same viewpoint.\n4. I have a question regarding the inference process. The paper states that `$f^{src}_{dense} can be used alone for inference. Could the authors elaborate on the reasoning behind this? Specifically:\n- Is the underlying assumption that the LoRA module inherits the capability to render fine-grained details?\n- Given that the input to `$f^{src}_{dense} does not seem to contain sufficient high-frequency information, how does the model generate these details effectively?\n5. Regarding the quantitative experiments, why were significantly fewer evaluation metrics used compared to those in GAGAvatar?\n\nI will consider raising my score after the authors have addressed my concerns.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### Strengths\n- The overall logic of the paper is fairly clear, and the writing is well-structured.\n- The core innovation lies in embedding and projecting additional vertex features of the 3D facial mesh through a register module, enabling a deep integration of geometric and visual features. Furthermore, detail interpolation and feature fusion are achieved using a convolutional network.\n\n### Weaknesses\n- Its strong dependency on the Register Module's 3DMM parameter accuracy means that errors in pose estimation (from occlusions or extreme angles) cascade, severely degrading the geometric and detailed accuracy of the final image. Another significant drawback is its inability to preserve clothing details, a capability present in earlier keypoint-based methods like FOMM.\n- Specific lighting effects from the source image, such as highlights and shadows, are highly prone to being \"baked\" into the model's texture map or implicit appearance representation. This makes them part of the character's intrinsic identity, which severely limits the model's realism and generalization capability under different lighting conditions.\n- During training, the reconstruction branch receives a highly processed feature, $f_{reg}$; however, during inference, it receives a more \"raw\" DINOv2 feature, $f_{dense}^{src}$. This distribution gap between the training and inference inputs is a potential cause for the model's sub-optimal performance at test time.", "was_revised": true, "final_justification": "This approach is clever and a valuable application of the register module for other fields.", "rebuttal": "We thank the reviewer for their thoughtful and constructive feedback. We are encouraged that they find the logic of our paper and idea clear, and recognize that our proposed method effectively addresses the limitations of traditional generalized avatar models and existing fine-tuning methods in detail preservation. We address the reviewer comments below and will incorporate all feedback in the revised manuscript. \n\n**Weakness 1: Dependency on pose estimation; cascading errors.**\nWe have not observed any significant degradation for the vast majority of driving frames. This is because we use the state-of-the-art 3DMM parameter estimation algorithms from Inferno:FaceReconstruction [4]. These estimated parameters do well for our purposes. However, the problem the reviewer pointed out might still exist in extreme poses and expressions, as we pointed out in Section 5 of the main paper and Section F.1 in the supplementary paper. Improving pose estimation is an interesting future work. Given the quality of our results, it is out of scope for this paper. \n\n**Clothing details.**\nCloth modeling is typically considered as a separate modeling problem, independent from face modeling. While FOMM might still be useful for cloth modeling, it is a 2D key-point method that is not as accurate as 3DMM parameters for fine facial motion control. Since our method aims to personalize 3D-aware generic avatar face generation models, cloth modeling is out of our scope. We regret the poor quality of any cloth visualizations; they are presented only for completeness of results. Instead, we point the reviewer to the quality of our results in detailed **face** features such as wrinkles, tattoos, freckles, etc., which is the focus of our research.\n\n**Weakness 2: Bakes source lighting into appearance, harming realism and generalization under new lighting.**\nOur results achieve preservation of specific textural details on faces (such as wrinkles, tattoos, freckles, etc.) in animatable avatars of a generalized avatar generation model. Our work is in line with lighting-agnostic methods (e.g., GAGAvatar[1], GPAvatar [6], Portrait4D [7], etc.) to model avatars. While modeling light for avatar relighting is interesting, it is a separate line of work (e.g., Portrait Video Relighting [8], Holo-Relighting [9]). We leave the task of incorporating lighting effects for the future. \n\n**Weakness 3: Training–inference feature mismatch.**\nAs described in L127-132, the Register Module is designed to learn a 3D feature space that captures identity-specific information and elicits such information from downstream trainable layers, similar to how registers work in Vision Transformers [10]. We remove the Register Module during inference, after it has served its purpose of \"teaching\" LoRA layers to retain fine facial details. This is similar to the way registers were proposed for ViTs [10]. They help store and repurpose information during training and are removed during inference. For completeness, we also tried the case when our Register Module is used during inference; however it harms the output quality - we see an increase in the ACD metric by around 5%.\n\n**Question 1: Regularization Loss for Register Embeddings.**\nThank you for pointing out a typo in the text. We would like to bring to attention that while equation 9 in the main paper is correct, the text in line 200 is incorrect. As the equation suggests, minimizing the objective $L_{reg} = \\frac{\\texttt{pcos}(e)}{n(V)(n(V)-1)}$ where $\\texttt{pcos}(X) = \\sum_i \\sum_j \\frac{X_i \\cdot X_j}{||X_i||||X_j||} - n(V)$ is the sum of the non-diagonal elements of a self-pairwise cosine distance, will result in embeddings $e_i$ to be ***dissimilar*** to each other. We will make a correction in the revised manuscript.\n\nWe experimentally found that $\\lambda_{reg} = 20$ performs best. We present the ablation study for this hyperparameter below on the VFHQTest dataset. \n| $\\lambda_{reg}$ | LPIPS↓ |   ACD↓  |\n|-----------------|--------|--------|\n|     1           | 0.2520 | 0.3616 |\n|     8           | 0.2502 | 0.3606 |\n|    16           | 0.2485 | 0.3594 |\n|    **20**       | **0.2470** | **0.3559** |\n|    32           | 0.2500 | 0.3663 |\n\n\n**Question 2: Qualitative Conclusions on Register Features.**\nAs in most current deep-learning methods that use latent spaces, it is impossible to tease out the effect of specific elements on the learnt features. This is the case of Figure 4 in the main paper and Figure 4-11 in the supplementary, where we visualize the projected embeddings. We posit that these features learn information in the 3D space that helps improve learning signals for LoRA, in order to capture fine identity-specific details. Approximating such black-box deep-learning features with networks that use interpretable features, while maintaining accuracy (as in SI-MIL [12]), is interesting future work.\n\n**Question 3 & Question 4: Rationale for Register Pipeline.**\nThe Register Module learns a 3D feature space that *stores and repurposes information* for a human face during training, as explained in L127-132. Inspired by registers for ViT [10] that store global information of an image, our Register Module stores the distinctive details of an identity, given different views. We sum the source features with features projected by the Register Module, in order to inject identity-specific information into the representation. \n\n**Question 5: Evaluation Metrics.**\nWe found that for our purpose of computing how well the fine details are preserved and the avatar is personalized to the input identity, metrics such as PSNR and SSIM were too unreliable. For example, for the case of the VFHQ Test dataset, we get the following results:\n\nNote: Best and second best results highlighted in **bold** and *italics* respectively.\n| Method        | PSNR↑      | SSIM↑      | LPIPS↓     | ACD↓        |\n|---------------|------------|------------|------------|-------------|\n| GAGAvatar     | **20.86**| *0.5921*   | *0.2540*      | *0.3631* |\n| LoRA          | 20.65    | 0.5699   | 0.2666     | 0.3687  |\n| MetaPortrait  | *20.71*    | **0.5975**| 0.2650  | 0.4131 |\n| Ours          | 20.30    | 0.5423   | **0.2470**| **0.3559** |\n\nMetaPortrait (meta-learning on LoRA weights) gives us the worst performance qualitatively, among all these methods, as can be seen in Figure 6 of the main paper, Figures 2-3 in the supplementary material, and the supplementary video. However, it fares 2nd best in PSNR, and best in SSIM. We believe this is because these metrics do not focus on specific features in faces, but pixel-level features. SSIM specifically is not necessarily designed with human perception in mind (Understanding SSIM [11]). Hence, we rely on the ACD metric and LPIPS, which rely on the perceptual similarity of identity and images, respectively.  \n\n## References\n[1] Chu et al., Generalizable and animatable Gaussian head avatar. In NeurIPS, 2024\n\n[2] Qian et al., Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians. In CVPR, 2024.\n\n[3] Dhamo et al., Headgas: Real-time animatable head avatars via 3d gaussian splatting. In ECCV, 2024.\n\n[4] Daněček et al., Inferno. https://github.com/radekd91/inferno?tab=readme-ov-file\n\n[5] Siarohin et al., ‘First Order Motion Model for Image Animation’, In NeurIPS, 2019.\n\n[6] Chu et al., ‘GPAvatar: Generalizable and Precise Head Avatar from Image(s)’, In ICLR 2024.\n\n[7] Deng,et al., ‘Learning One-Shot 4D Head Avatar Synthesis using Synthetic Data’, In CVPR 2024.\n\n[8] Rao et al., ‘Lite2Relight: 3D-aware Single Image Portrait Relighting’, in Proceedings of ACM SIGGRAPH 2024.\n\n[9] Mei et al., ‘Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image’, arXiv preprint arXiv:2403. 09632, 2024.\n\n[10] Darcet, et al., ‘Vision Transformers Need Registers’, in ICLR, 2024.\n\n[11] Nilsson, et al., ‘Understanding SSIM’, arXiv [eess.IV]. 2020.\n\n[12] S. Kapse et al., ‘SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology’, arXiv preprint arXiv:2312. 15010, 2023."}, {"review_id": "wUA80hlrqc", "reviewer": "Reviewer_wF51", "summary": "This paper addresses the challenge of personalizing generic, 3D-aware head avatar models to reconstruct the unique, high-frequency details of an unseen individual (e.g., wrinkles, tattoos, skin texture). The authors posit that standard adaptation techniques like Low-Rank Adaptation (LoRA) are insufficient for this task, as they tend to lose these identity-specific features.\n\nThe core contribution is a novel Register Module, designed to augment LoRA-based personalization. Inspired by registers in Vision Transformers that store global information, this module introduces a learnable 3D feature space that operates on intermediate features of a pre-trained model. The purpose of this module is to explicitly capture, store, and re-purpose the distinctive facial details of a target identity during the adaptation process.\n\nTo validate their method, the authors show through quantitative and qualitative examples that their approach outperforms standard LoRA and a meta-learning baseline in terms of both visual quality and identity preservation.", "questions": "**Clarification of the Register Module**: Could you please provide a more detailed architectural description of the Register Module? The CNN-based encoder is mentioned but not illustrated. A small diagram illustrating how it takes intermediate features as input, processes them through the 3D learnable space, and outputs the enhanced features would be immensely helpful. \n\n**Quantify Efficiency Claims**: You claim the method is efficient in terms of parameters and speed. Could you please update the paper to include:\n\n(a) The number of trainable parameters for the vanilla LoRA baseline versus your method (LoRA + Registers).\n\n(b) The impact on inference speed (e.g., frames per second or milliseconds per frame) for both methods.\n\n\n**Temporal Consistency**: It seems that the register module rely heavily on the 3DMM prediction, does the video-level prediciton is done frame-wise? How does the authors improve temporal consistency?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**:\n\n**Well-Motivated Problem**: The paper targets a clear and significant problem. As generic avatar models become more common, the need for high-fidelity, efficient personalization is crucial. The observation that vanilla LoRA fails to capture high-frequency details is an excellent and practical motivation for this work.\n\n**Simple and Intuitive Technical Contribution**: The proposed \"Register Module\" is a clever idea. Adapting the concept of \"registers\" from 2D Transformers to a 3D feature space for identity preservation is an insightful contribution. The intuition that LoRA's low-rank updates might be too coarse to handle fine details, thus requiring a dedicated \"memory\" or \"cache\" for them, is compelling.\n\n\n**Weaknesses**:\n\n**Lack of Architectural Detail**: The paper is surprisingly light on the specific architectural details of the Register Module. While the high-level concept is explained, it is unclear how this 3D feature space is implemented, how it interacts with the intermediate features of the DINOv2 backbone, and how information is \"stored\" and \"re-purposed\". This lack of clarity makes it difficult to fully assess the technical novelty and replicate the work from the paper alone.\n\n**Unsubstantiated Efficiency Claims**: The abstract and introduction claim the method requires \"a small number of parameters\" and retains \"the original inference speed.\" These are important claims for a personalization method but are not substantiated with data. The paper needs to report the parameter count of the Register Module and provide concrete numbers on the impact on inference time (e.g., FPS) compared to the vanilla LoRA baseline.\n\n**Limited Discussion on Generalizability**: The method is presented and validated on a single backbone architecture (GAGAvatar). While effective, this raises questions about the generalizability of the Register Module. A discussion on whether this module is a general-purpose \"plug-in\" for other 3D-aware generative models would significantly broaden the paper's impact.", "was_revised": true, "final_justification": "Based on the authors' thoughtful response, most of my initial concerns have been resolved, so I'm comfortable raising my score", "rebuttal": "We sincerely thank the reviewer for their thoughtful and detailed feedback. We are encouraged that they found our problem motivation strong and practical, particularly the need for high-fidelity personalization beyond what standard LoRA provides. We are pleased the reviewer appreciated the simplicity and intuitiveness of the Register Module, and found the adaptation of Transformer-style registers to a 3D feature space to be a novel and insightful idea. We are also glad they recognized the effectiveness of our method in capturing identity-specific high-frequency details and outperforming both standard LoRA and a meta-learning baseline. We address the reviewer comments below and will incorporate all feedback in the revised manuscript.\n\n**Weakness 1 & Question 1: Lack of Architectural Detail.**\nWe thank the reviewer for their suggestion. We will highlight the architectural details and the \"forward\" process in the revised manuscript. We would like to note that we explain the entire \"forward\" process in Section 3 of the main paper and give additional architectural details of the register module in Section B.3 of the supplementary material. Furthermore, we illustrate our register module in Figure 3. Each vertex in a 3D face mesh is rigged with an embedding, which gets projected to the screen space according to the camera pose, which gives us the 3D feature space on a 2D screen space. These embeddings capture identity-specific information and elicit such information from downstream trainable layers, similar to how registers work in Vision Transformers [3]. We remove the register module during inference, after it has served its purpose of \"teaching\" LoRA layers to retain fine details.\n    \n**Weakness 2 & Question 2: Unsubstantiated Efficiency Claims.**\nWe thank the reviewer for their comment. We will incorporate changes in the revised manuscript to highlight the fact that the following two points: (a) The register module is only used during training, as stated in L211-212, and (b) The trained LoRA layers are merged with the original parameters for inference using Eq. 1 in Section 3.2. As a consequence of this implementation, our method retains the exact efficiency of the original model during inference, as does the vanilla LoRA variant. \n\nWe provide a table of parameter counts below:\n\n\n| Module Name                      | Parameter Count during Training | Parameter Count during Inference | Inference Time for Variant (FPS) |\n| -------------------------------- | ------------------------------------- | -------------------------- | --------------------------------- |\n| Original Model (w/ DINOv2)       | 199,430,518                           | 199,430,518                 | 34                                |\n| Model + LoRA Layers                      | 204,193,142                             |  199,430,518                 | 34                                |\n| Meta Learning on LoRA            | 204,193,142                            | 199,430,518                  | 34                                |\n| Model + LoRA Layers + 3D Register Module | 222,772,086                            | 199,430,518                 | 34                                |\n\n\nNote that during inference, all variant baselines have the same efficiency, due to the reasons explained above. The FPS numbers are measured based on an Nvidia A6000 GPU. \n    \n**Weakness 3: Limited Discussion on Generalizability.**\nWe believe that our approach of low-rank personalization is extendable to other approaches because of the use of DINOv2 features, which are general domain features and commonly used in various works. We specifically chose GAGAvatar to demonstrate our method because it is a very competitive method, having been trained on a very large dataset (VFHQ [1]), and producing state-of-the-art results in the general avatar domain.\n\n**Question 3: Temporal Consistency.**\nThe video-level prediction is done frame-by-frame. We focus on personalizing GAGAvatar [2] to capture identity-specific details, and thus we do not change its basic architecture. We process the predicted 3DMM parameters using exponential smoothing as in GAGAvatar [2], thereby ensuring temporal consistency in output frames.\n\n    \n## References\n[1] Xie et al., Vfhq: A high-quality dataset and benchmark for video face super-resolution. In CVPR Workshops (CVPRW), 2022.\n\n[2] Chu et al., Generalizable and animatable gaussian head avatar. In NeurIPS 2024.\n\n[3] Darcet et al., ‘Vision Transformers Need Registers’, In ICLR 2024."}, {"review_id": "etDUsLC73Y", "reviewer": "Reviewer_k9TX", "summary": "The paper presents a new method for personalizing head avatar models through a LoRA approach enhanced by a learnable Register Module. This module introduces a 3D feature space that captures high-frequency identity-specific facial details such as wrinkles and tattoos, which are often missing in generic models trained on large datasets. Additionally, the authors introduce a new dataset, RareFace-50, to demonstrate the effectiveness of their approach. Quantitative and qualitative evaluations on RareFace-50 and VFHQ Test datasets show improvements over baseline and naive LoRA methods.", "questions": "I would like the author to answer the following questions in order to further enhance the score:\n\n-  In the feature learning procedure starting from Line 169, why do you adopt the process of computing visible joints → projection → interpolation, instead of using a rasterization-based approach? Rasterization is a standardized and GPU-efficient method that is typically much faster and more scalable.\n- Why didn’t you evaluate your method on more datasets and with additional baseline methods (other than GAGAvatar)? Is the proposed approach only applicable to GAGAvatar?", "limitations": "- The proposed method still requires annotated 3DMM parameters for adaptation, which might limit its accessibility or deployment in the wild. This also makes the method inefficient for personalizing the head avatar. \n\n- Currently, the proposed method is only demonstrated on GAGAvatar. The authors provide no evidence of its applicability to other human head avatar methods, which limits the generalizability of the approach.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The paper is well-written and structured, making it easy to understand.\n- The proposed 3D register extension is effective for the personalization training of the face avatar.\n- Introduction of RareFace-50 fills a valuable gap for testing on underrepresented facial features.\n\nWeaknesses: \n- The technical novelty is limited: the approach primarily reuses LoRA, with the main contribution being the use of registers. However, the register mechanism closely resembles that of the recent SIGGRAPH paper LAM, which also leverages features from 3DMM geometry.\nLAM: https://github.com/aigc3d/LAM\n\n- In the experiments, the authors only apply the proposed method to the GAGAvatar method, while not proving the generalized applicability of the proposed method based on other head avatar methods.\n\n- In the experiments, the authors evaluate their method on only two datasets—VFHQ and RareFace-50—without including comparisons on other relevant datasets such as HDTF (used in GAGAvatar) or Nersemble, which is now widely adopted.\n\nHDTF: https://github.com/MRzzm/HDTF\nNersemble: https://tobias-kirschstein.github.io/nersemble/", "was_revised": true, "final_justification": "The rebuttal can resolve most of my concerns. However, I am still concerned about the technical contributions of this paper. Considering these, I would like to keep my rating as borderline accept.", "rebuttal": "We thank the reviewer for their detailed and encouraging feedback. We are pleased they found our paper well-written and easy to follow, and appreciated the structure and clarity of our presentation. We are glad the effectiveness of our 3D Register Module for enhancing identity-specific facial details was recognized, as well as the meaningful role it plays in improving personalization. We also appreciate the acknowledgment of RareFace-50 as a valuable contribution that helps address the underrepresentation of distinctive facial features in current benchmarks. We address the reviewer comments below and will incorporate all feedback in the revised manuscript.\n    \n**Weakness 1: Limited Novelty Due to Similarity with LAM and Reuse of LoRA.**\nLAM [1] is a one-shot head avatar model that produces an animatable Gaussian head avatar from a single image. Our goal in this paper is to personalize a generalized head-avatar model to a particular identity, such that high-frequency details are preserved. LAM focuses on animating a single image, while we personalize a single image animation model using a short clip. \n\nFurthermore, our model design is entirely different from LAM's, which rigs embeddings to the vertices of a 3DMM's mesh with cross-attention layers to learn from DINOv2 features. In contrast, our register module operates in a learnable 3D feature space and projects back to the 2D space for facilitating LoRA adaptation, and is only used during training.\n    \n**Weakness 2, Weakness 3 & Question 2: Limited dataset and baseline evaluation.**\nWe believe that our approach of low-rank personalization is extendable to other approaches since we use DINOv2 features, which are general domain features and commonly used in various works. We specifically chose GAGAvatar to demonstrate our method because it is a very competitive method, having been trained on a very large dataset (VFHQ [1]), and producing state-of-the-art results in the general avatar domain. \n\nVFHQ is a large-scale and high-quality video face dataset. It includes diverse identities, facial expressions, head poses, and settings, and thus it is used in recent works. On the other hand, we collected RareFace-50 to include unique facial details that are underrepresented or do not even appear in existing datasets.\nWe also provide the evaluation metrics on the HDTF dataset for all baselines and our method below.\nNote: Best and second-best results highlighted in **bold** and *italics* respectively.\n| Method       |   LPIPS↓   |   ACD↓      |\n|--------------|------------|------------|\n| GAGAvatar    | *0.1747*  | *0.3441*   |\n| LoRA         | 0.1770    | 0.3553     |\n| MetaPortrait | 0.1901    | 0.3559     |\n| Ours         | **0.1618** | **0.3156** |\n\nNote that the NeRSemble [2] is a multi-view high-quality lab-setting video dataset used for the 3D multi-view face reconstruction task. This dataset is used when the method needs multi-view images and videos. We used other datasets that have in-the-wild monocular videos, which are suitable for our setting.  \n    \n\n**Question 1: Rasterization instead of visible vertices → projection → interpolation.**\nWe appreciate this interesting suggestion as one implementation option. Since this pipeline is not used during inference, it does not affect the speed or the scalability of the method. While we believe it does not have a critical effect on our results, we will explore this option as future work.\n\n## References\n[1] He et al., ‘LAM: Large Avatar Model for One-shot Animatable Gaussian Head’, in SIGGRAPH, 2025.\n\n[2] Kirschstein et al., ‘NeRSemble: Multi-View Radiance Field Reconstruction of Human Heads’, ACM Trans. Graph., vol. 42, no. 4, Jul. 2023."}, {"review_id": "p124oNGCTg", "reviewer": "Reviewer_QQ5r", "summary": "This paper tackles the problem of personalizing head avatars by adapting a pretrained generalized avatar generation model to a target subject using short videos of 4-15 seconds. The key technical contribution is a \"Register Module\" that enhances low-rank adaptation (LoRA) by combining local per-vertex embeddings with DINO-v2 features. The authors also contribute a video dataset of 50 subjects with distinct facial features for evaluation. They compare their method against 2 other approaches using perceptual quality (LPIPS) and identity similarity (Arcface embeddings) metrics. I would consider the potential impact as limited due to its specificity to LoRA finetuning of pretrained generalized avatar models.", "questions": "- Ablations\n    - How does the method compare numerically to LoRA and its extensions? Specifically, what are the metrics when comparing LoRA vs. the proposed LoRA+Register Module vs. Dora vs. Dora+Register Module?\n- Comparisons\n    - How does the method compare against the state-of-the-art in avatar generation from videos (SplattingAvatar, FlashAvatar, and MonoGaussianAvatar)?", "limitations": "I appreciate mentioning the potential issues about imperfect 3DMM fitting (Sec. F.1). I would also include a note about the expected diversity in the input video sequence, specifically to what extent the method is robust against limited expression and head pose diversity.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n+ The results with LoRA alone are indeed overly blurry and a better personalization method is needed.\n+ The results after personalization improve details over the baseline (GAGAvatar)\n+ The paper is easy to read.\n+ The paper promises to release the collected dataset.\n\n## Weaknesses\n- Minimal Ablation\n    - The ablation study is minimal and fails to analyze the most important component of the proposed method. The ablation study adds noise (a) and learnable embeddings to the DINO features f_\\text{sense}^\\text{src}, i.e., it studies the effect of the DINO input features (Sec. 4.3, Fig. 3). The correct component to study, however, would be the proposed register module (blue box in Fig. 3). A more meaningful ablation study could replace the learned vertex features with noise or the xyz positions or it could ablate the need for processing the rasterized features with E_\\text{proc-1}. At minimum, the ablation study should provide numbers for the model variant that uses LORA vs. LORA + registers. \n- Limited comparisons\n    - The paper proposes a method for talking head generation from a video. There are dozens of relevant works in the literature, but the paper only compares with a feedforward method (GagAvatar) without personalization and a more generic method, which does not use a 3D Morphable Face Model (LoRA). Given the similar methodology, the comparison with GagAvatar and LoRA is important, but the comparisons should include at least the state-of-the-art for avatar generation from videos like SplattingAvatar, FlashAvatar, and MonoGaussianAvatar. Optional comparisons could be conducted by fine-tuning EG3D using Pivotal Tuning Inversion  \n- Missing citations\n    - The paper does not cite other relevant work in avatar personalization, e.g., by leveraging meta-learning (Tancik et al. 2021), data-driven priors (Buehler et al. 2024), or extensions to LoRA (Liu et al. 2024).\n\n\n## References\n\n      Roich, Daniel, et al. \"Pivotal tuning for latent-based editing of real images.\" __ACM Transactions on graphics (TOG)__ 42.1 (2022): 1-13.\n      Chen, Yufan, et al. \"Monogaussianavatar: Monocular gaussian point-based head avatar.\" __ACM SIGGRAPH 2024 Conference Papers__. 2024.\n      Shao, Zhijing, et al. \"Splattingavatar: Realistic real-time human avatars with mesh-embedded gaussian splatting.\" __Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition__. 2024.\n      Xiang, Jun, et al. \"Flashavatar: High-fidelity head avatar with efficient gaussian embedding.\" __Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition__. 2024.\n      Tancik, Matthew, et al. \"Learned initializations for optimizing coordinate-based neural representations.\" __Proceedings of the IEEE/CVF conference on computer vision and pattern recognition__. 2021.\n      Buehler, Marcel C., et al. \"Cafca: High-quality Novel View Synthesis of Expressive Faces from Casual Few-shot Captures.\" __SIGGRAPH Asia 2024 Conference Papers__. 2024.\n      Liu, Shih-Yang, et al. \"Dora: Weight-decomposed low-rank adaptation.\" __Forty-first International Conference on Machine Learning__. 2024.", "was_revised": true, "final_justification": "The added ablations and comparisons resolved my major concerns. I particularly appreciate the ablation of DORA and the comparison with Splattingavatar.\n\nIn summary, the paper proposes registers, which is shown to be an effective contribution to LORA and DORA and outperforms related works.", "rebuttal": "We thank the reviewer for their thoughtful feedback. We are encouraged that they found our Register Module to be a key technical contribution. We are glad the reviewer recognized the clarity of the paper, the perceptual and identity improvements over the baseline (GAGAvatar), and the value of releasing our collected dataset. We also appreciate their observation that LoRA alone produces blurry results, highlighting the importance of improved personalization strategies. We address the reviewer comments below and will incorporate all feedback in the revised manuscript.\n\n**Weakness 1 & Question 1: Ablations.**\nWe thank the reviewer for their comments. We will incorporate the LoRA baseline results in our ablations table (Table 1) to highlight the difference in the ablations study. We present those results in the table below. We would like to mention that these results are present in Table 2 of the main paper in row 2 (LoRA variant) and row 4 (LoRA w/ register variant) on the VFHQ Test and RareFace-50 datasets. We also present those results in the table below.\n\nThe ablation to use xyz coordinates instead of learnable embeddings for vertices in the register module is an interesting approach. Prior approaches, such as NeRF [1] and mip-NeRF [2], use multi-layer perceptrons (MLPs) and positional encoding (PE) to inflate the dimensions of the 3-dimensional input. Using an MLP+PE approach will give us a learnable embedding for each vertex, thus making it similar to our proposed approach.\n    \nWe present the results for the suggested ablation of replacing learnable embeddings with Gaussian noise below. We observe that using Gaussian noise embeddings leads to an improvement over variant (a) LoRA. However, they still perform worse than variant (b) LoRA w/ Reg (Ours). Qualitatively, we see higher detail preservation as compared to variant (a), but worse detail retention than our method.\n\nWe also present the results with DoRA: \\(c) DoRA [3] and (d) our register module with DoRA. We note that the performance of \\(c) DoRA is inferior to (a) LoRA. However, introducing our register module during training improves its performance. We also see a similar trend qualitatively.\n\nNote that the constructed dense feature $f_{S}$ has $D=512$ channel dimensions (refer to Ln 288 onwards). Since $f_{dense}^{src}$ features have $256$ channel dimensions, we use $E_{proc-1}$ to scale down the dimensions of $f_{S}$ using $f_{proc-1} = E_{proc-1}(f_{S})$ to support the operation $f_{proc-1}+f_{dense}^{src}$. Hence, using $E_{proc-1}$ is necessary. \nNote: Best and second-best results highlighted in **bold** and *italics* respectively.\n\n| Method                   | LPIPS↓   | ACD↓   |\n|--------------------------|------------|------------|\n| (a) LoRA                     | 0.2666     | 0.3687     |\n| (b) LoRA w/ Reg (Ours)       | **0.2470** | **0.3559** |\n| \\(c) DoRA                     | 0.2685    | 0.3853    |\n| (d) DoRA w/ Reg              | 0.2668    | 0.3702    |\n| (e) Gaussian Noise Vertex Embeddings | 0.2493    | 0.3623   |\n\n\n**Weakness 2 & Question 2: Missing Comparisons.**\nWe thank the reviewers for their suggestions. We note that the papers referred to by the reviewer, SplattingAvatar [4], FlashAvatar [5], and MonoGaussianAvatar [6], are head reconstruction methods, which have a different setting from our method. Head reconstruction methods either take multi-view or single-view videos to train a NeRF or variant (e.g., Tri-Planes, Gaussian Splatting, etc), in order to learn the 3D representation of a *single identity*. In other words, they require separate from-scratch training for each identity on long videos (5-10 mins) and are sensitive to camera calibration errors. We, on the other hand, take a *generative* 3D-aware one-shot avatar model that is trained on a large-scale in-the-wild dataset and can generate any identity. We propose to personalize it using a short in-the-wild video (5-10 seconds) to preserve identity-specific and high-frequency details.\n    \n**Weakness 3: Missing Citations.**\nWe thank the reviewer for the relevant citations and will gladly incorporate them into our revised manuscript. Cafca [7] uses data-driven priors for novel-view synthesis of expressive faces; however, it does not generate face animations. Tancik et al. [8] use meta-learning to initialize weights in coordinate-based neural networks, with interesting applications, including image regression (but not face animation as in our paper). DoRA [3] is an extension to LoRA, which we have incorporated into our method and compared with.\n\n## References\n[1] Mildenhall et al., ‘NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis’, in ECCV, 2020.   \n\n[2] Barron et al., ‘Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields’, ICCV, 2021.\n\n[3] Liu et al., ‘DoRA: Weight-Decomposed Low-Rank Adaptation’, in ICML, 2024.\n\n[4] Shao et al., ‘SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting’, in CVPR, 2024.\n\n[5] Xiang et al., ‘FlashAvatar: High-fidelity Head Avatar with Efficient Gaussian Embedding’, in CVPR, 2024.\n\n[6] Chen et al., ‘Monogaussianavatar: Monocular gaussian point-based head avatar’, in ACM SIGGRAPH 2024 Conference Papers, 2024, pp. 1–9.\n\n[7] M. C. Buehler et al., ‘Cafca: High-quality Novel View Synthesis of Expressive Faces from Casual Few-shot Captures’, in ACM SIGGRAPH Asia 2024 Conference Paper, 2024.\n[8] M. Tancik et al., ‘Learned Initializations for Optimizing Coordinate-Based Neural Representations’, in CVPR, 2021."}]

Author rebuttal:

Meta-review: The paper studies head avatar generation, and proposes a register module that enhances the performance of LoRA adapters, while requiring only a small number of parameters to adapt to an unseen identity. Initial reviews raised several issues, including clarifications of the contributions, limited empirical evidence, and related work comparisons. The rebuttal successfully addressed most of these concerns, leading to two reviewers raising their scores. After careful deliberation, the decision was made to accept this paper - congratulations to the authors! It is crucial, however, that all provided clarifications are incorporated in the final version of the paper.
Decision: Accept (poster)