LIA-X: Interpretable Latent Portrait Animator

## Abstract

Abstract We introduce LIA-X, a novel interpretable portrait animator designed to transfer facial dynamics from a driving video to a source portrait with fine-grained control. LIA-X is an autoencoder that models motion transfer as a linear navigation of motion codes in latent space. Crucially, it incorporates a novel Sparse Motion Dictionary that enables the model to disentangle facial dynamics into interpretable factors. Deviating from previous ’warp-render’ approaches, the interpretability of the Sparse Motion Dictionary allows LIA-X to support a highly controllable ’edit-warp-render’ strategy, enabling precise manipulation of fine-grained facial semantics in the source portrait. This helps to narrow initial differences with the driving video in terms of pose and expression. Moreover, we demonstrate the scalability of LIA-X by successfully training a large-scale model with approximately 1 billion parameters on extensive datasets. Experimental results show that our proposed method outperforms previous approaches in both self-reenactment and cross-reenactment tasks across several benchmarks. Additionally, the interpretable and controllable nature of LIA-X supports practical applications such as fine-grained, user-guided image and video editing, as well as 3D-aware portrait video manipulation.

## 1 Introduction

With the remarkable development of deep generative models [13, 32, 10], techniques for video generation have been largely advanced. Portrait animation, a domain-specific video generation task that aims to transfer facial dynamics from a driving video to a portrait image, has also received increasing attention due to its wide applications in entertainment, e-education, and digital human creation.

Towards accurately transferring facial dynamics, one straightforward strategy is to leverage pre-computed explicit representations such as facial landmarks [39, 5, 21], 3DMM [19, 6], optical flows [17, 23] and dense poses [50] as motion guidance. Such an approach has been widely used in both GAN-based and recent diffusion-based methods [5, 21]. However, the generated quality of these methods relies heavily on the performance of the off-the-shelf feature extractors, which strongly restricts their usability in more challenging, real-world scenarios.

Self-supervised learning-based techniques have also been proposed to tackle this problem. Previous methods explored learning either explicit structures such as 2D/3D keypoints [28, 29, 58, 40, 11] or implicit motion codes [48, 45] in an end-to-end manner to model facial dynamics. They typically follow a ‘warp-render’ strategy to conduct portrait animation based on the computed optical-flow fields. While these methods have achieved promising results in both motion transferring and identity preservation, their performance still drops significantly when large variations exist between the source and driving data in terms of head poses and facial expressions.

Towards addressing this issue, we introduce LIA-X, a novel framework that learns interpretable motion semantics to enable a highly controllable ‘edit-warp-render’ animation strategy. Deviating from the standard ‘warp-render’ pipeline, LIA-X allows users to first leverage the learned motion representations to align source portrait with the initial driving frame before applying the final motion transfer (see Fig. [1](https://arxiv.org/html/2508.09959v1#S0.F1)).

Specifically, LIA-X is designed as a self-supervised autoencoder that does not rely on any explicit structure representations. Inspired by sparse dictionary coding [24], we incorporates a novel Sparse Motion Dictionary - a set of motion vectors in autoencoder to capture the underlying motion distribution. The sparsity constraint encourages the model to use a minimal set of these motion vectors to reconstruct the training images, endowing the motion vectors with enhanced interpretability compared to the dense motion dictionary proposed in prior work [48, 45]. The animation process is then modeled as a linear navigation of these motion codes to produce optical-flow fields for warping the source portrait. Interestingly, we found that these interpretable motion vectors can also be directly leveraged at the inference stage to manipulate source portrait, supporting fine-grained edits of facial attributes (e.g., eyes and mouth) as well as 3D-aware transformation (e.g., yaw, pitch and roll) in both image and video domains.

In addition, we have analyzed the scalability of proposed LIA-X framework and successfully train large-scale models with up to 1 billion parameters using a diverse mixture of public and internal talking head datasets. Experimental results demonstrate that scaling up the LIA-X significantly improves performance across several benchmarks. Given the faster inference speed of autoencoder-based models compared to diffusion-based approaches, we believe the scalable and interpretable design of LIA-X can serve as a valuable complement to current state-of-the-art generative models, enabling efficient and highly controllable video generation. In summary, the key contributions of this work are:

- •
We propose a novel portrait animator, LIA-X, an autoencoder that incorporates an interpretable Sparse Motion Dictionary to enable controllable portrait animation via an ‘edit-warp-render’ strategy.
- •
We analyze the scalability of the LIA-X and demonstrate that our design can be scaled up to achieve superior performance.
- •
Extensive experiments show that LIA-X outperforms state-of-the-art methods across several datasets, while also supporting a various of applications such as image and video editing, and 3D-aware portrait manipulation.

Figure: Figure 2: Overview of LIA-X. LIA-X consists of an encoder $E$, a generator $G$ that includes an optical-flow generator $G_{f}$ and a rendering network $G_{r}$. LIA-X is trained using a self-supervised learning strategy. To obtain an interpretable motion dictionary $D_{m}$, a sparsity constraint is incorporated into the training objective, enrouraging the network to use a minimal number of vectors in $D_{m}$ to reconstruct each driving image.
Refer to caption: imgs/general.png

## 2 Related Work

Portrait animation has seen significant advancements in recent years, driven by the remarkable progress in deep generative models for video generation [36, 26, 35, 43, 42, 41, 44, 9, 2, 53, 31, 34, 47, 8, 7, 12, 30, 14, 20, 46, 1, 22, 51, 3, 56]. Previous methods have explored learning structure representations either based on conditional generation approaches [4, 38, 55, 39, 52, 58] relying on off-the-shelf extractors, or self-supervised learning strategies to learn representations such as 2D/3D keypoints [28, 40, 11, 58], motion regions [29], and depth maps [16] in an end-to-end manner. Self-attention mechanism [54] has also been studied to improve cross-identity generation quality.

More recent techniques [48, 45] have proposed to model motion transfer as a linear navigation of learned motion codes, which has proven effective in implicitly capturing 2D and 3D representations to capture complex facial dynamics. Diffusion-based models [49] have also been shown to be effective for portrait animation, due to their powerful generalization abilities pretrained on large-scale datasets.

However, one key limitation still exists across all the existing methods, they lack an effective mechanism to align the source portrait with the initial driving frame in terms of head poses and facial expressions. This makes their performance drop dramatically when there are large variations between the source and driving data, further limiting their usability in more general real-world scenarios. Different from previous approaches, LIA-X incorporates an interpretable motion dictionary, which allows users to edit source portrait before animation in a controllable manner. With this unique capacity, we found LIA-X can be further used in several tasks such as portrait animation, image and video editing, and 3D-aware portrait manipulation.

## 3 Preliminary

Latent Image Animator (LIA) [48, 45] is designed as an autoencoder consisting of an encoder E, an optical-flow generator $G_{f}$ and a rendering network $G_{r}$ aiming to transfer motion of a talking head to a still portrait via self-supervised learning. LIA models motion as linear navigation of motion code in latent space, and follows the ‘warp-render’ strategy to generate optical-flow fields via $G_{f}$ and render the animated result via $G_{r}$.

Linear Navigation. LIA models motion transferring as learning transformations from source to driving image $x_{s}\rightarrow x_{d}$. It proved that for any given image, there exists an ‘implicit reference image’ $x_{r}$, and the transformation can be modeled as $x_{s}\rightarrow x_{r}\rightarrow x_{d}$ in an implicit manner. The transformation is modeled as motion code $z_{s\rightarrow d}$ in latent space, and with the help of the reference space, it can be represented as a linear navigation from $z_{s\rightarrow r}$ along a path $w_{r\rightarrow d}$, denoted as

$$ $z_{s\rightarrow d}=z_{s\rightarrow r}+w_{r\rightarrow d},$ (1) $$

where $z_{s\rightarrow r}$ indicates the transformation from source image to reference image, and is obtained via $E(x_{s})=z_{s\rightarrow r}$. To learn $w_{r\rightarrow d}$, a learnable motion dictionary $D_{m}$ consisting of a set of orthogonal motion vectors $\{\mathbf{d_{1}},...,\mathbf{d_{M}}\}$ is proposed. Any linear path in the latent space can be represented as

$$ $w_{r\rightarrow d}=\sum^{M}_{i=1}a_{i}\mathbf{d_{i}},$ (2) $$

where $\mathcal{A}_{r\rightarrow d}=\{a_{1},...,a_{M}\}$ indicates the magnitude for each motion vector, and is obtained via $\mathcal{FC}(E(x_{d}))=\mathcal{A}_{r\rightarrow d}$. The linear navigation from any $x_{s}$ to $x_{d}$ can be represented as

$$ $z_{s\rightarrow d}=z_{s\rightarrow r}+\sum^{M}_{i=1}a_{i}\mathbf{d_{i}}.$ (3) $$

Image Animation. Once $z_{s\rightarrow d}$ is obtained, optical flows are generated via $G_{f}(z_{s\rightarrow d})=\phi_{s\rightarrow d}$. The source image $x_{s}$ is warped based on $\phi$ and the warped features are rendered through $G_{r}$ to produce the generated image

$$ $x_{s\rightarrow d}=G_{r}(\mathcal{T}(\phi_{s\rightarrow d},x_{s})),$ (4) $$

where $\mathcal{T}$ indicates the warping operation. In practice, multi-scale optical flows are generated to warp multi-scale feature maps of $x_{s}$.

Learning. The objective of self-supervised learning consists of three parts, an $L_{1}$ reconstruction loss, a VGG-based perceptual loss and an adversarial loss between $x_{s\rightarrow d}$ and $x_{d}$

$$ $\begin{split}\mathcal{L}(x_{s\rightarrow d},x_{d})=\mathcal{L}_{recon}(x_{s\rightarrow d},x_{d})&+\lambda\mathcal{L}_{vgg}(x_{s\rightarrow d},x_{d})\\ &+\mathcal{L}_{adv}(x_{s\rightarrow d}).\end{split}$ (5) $$

Inference. LIA proposed a unified formulation for self-reenactment and cross-reenactment at the inference stage

$$ $\begin{split}z_{s\rightarrow t}=(z_{s\rightarrow r}+w_{r\rightarrow s})+(w_{r\rightarrow t}-w_{r\rightarrow 1}),\;t\in\{1,...,T\},\end{split}$ (6) $$

For self-reenactment, where $w_{r\rightarrow s}=w_{r\rightarrow 1}$, Eq. [6](https://arxiv.org/html/2508.09959v1#S3.E6) can be simplified as

$$ $z_{s\rightarrow t}=z_{s\rightarrow r}+w_{r\rightarrow t},\;t\in\{1,...,T\},$ (7) $$

which is the same as the training stage.

For cross-reenactment, where $w_{r\rightarrow s}\neq w_{r\rightarrow 1}$, Eq. [6](https://arxiv.org/html/2508.09959v1#S3.E6) can be rewritten as

$$ $\begin{split}z_{s\rightarrow t}=\underbrace{z_{s\rightarrow s}}_{\text{reconstruction}}+\underbrace{(w_{r\rightarrow t}-w_{r\rightarrow 1})}_{\text{motion difference}},\;t\in\{1,...,T\},\end{split}$ (8) $$

where the animation process is modeled as linearly navigating the source image along the driving motion direction in the latent space.

## 4 Methodology of LIA-X

In this section, we proceed to introduce our proposed LIA-X, including the general model architecture, the Sparse Motion Dictionary, as well as the interpretable and controllable animation capabilities.

### 4.1 Architecture

LIA-X follows the general architecture design of LIA, containing an encoder $E$, an optical-flow generator $G_{f}$, and a rendering network $G_{r}$. However, deviating from previous methods [28, 29, 58] that rely on simple and small-scale networks, we design the LIA-X architecture to be more scalable by incorporating advanced residual blocks inspired by StyleGAN-T [27] in both the encoder and generators.

### 4.2 Sparse Motion Dictionary

While the original motion dictionary in LIA contains certain semantic meanings, we observed that it is difficult to disentangle and independently control individual factors such as mouth movements, eyebrow expressions, etc. Multiple semantics are entangled and hard to leverage for controllable image and video editing tasks.

Inspired by sparse dictionary coding, we propose a Sparse Motion Dictionary to improve the interpretability of the motion representations. We found that this simple improvement is extremely effective in enhancing the interpretability of the motion vectors, which in turn enables more controllable animation.

Specifically, we introduce a sparse penalty $S(\cdot)$ on the motion coefficients $\mathcal{A}_{r\rightarrow d}$ to encourage the model to use a minimal number of motion vectors to reconstruct the images during self-supervised training. The overall learning objective of LIA-X is then:

$$ $\begin{split}\mathcal{L}(x_{s\rightarrow d},x_{d})=&\mathcal{L}_{recon}(x_{s\rightarrow d},x_{d})+\lambda_{1}\mathcal{L}_{vgg}(x_{s\rightarrow d},x_{d})\\ &+\mathcal{L}_{adv}(x_{s\rightarrow d})+\lambda_{2}S(\mathcal{A}_{r\rightarrow d}),\end{split}$ (9) $$

where $\lambda_{1}$ and $\lambda_{2}$ are coefficients to balance the losses, and we implement $S(\cdot)$ as the $L_{1}$ norm.

### 4.3 Controllable Inference

While Eq. [8](https://arxiv.org/html/2508.09959v1#S3.E8) enables successful motion transfer from the driving video to the source portrait, the high-quality generation relies on a strong requirement - the source and initial driving frames should have similar head poses and facial expressions. Ideally, portraits with frontal pose and neutral expression usually result in the best performance. However, in various real-world applications, this requirement cannot always be satisfied. With the interpretable Sparse Motion Dictionary, LIA-X is able to address this issue by using an ”edit-warp-render” strategy.

Users are first allowed to edit the source portrait using the corresponding motion vectors, aligning the head pose and facial expression to be as similar as possible to the driving image. Then, the animation process can be formulated as:

$$ $z_{s\rightarrow t}=\underbrace{z_{s\rightarrow\mathcal{E}(s)}}_{\text{editing}}+\underbrace{(w_{r\rightarrow t}-w_{r\rightarrow 1})}_{\text{motion difference}},\;t\in\{1,...,T\},$ (10) $$

where $\mathcal{E}(\cdot)$ indicates the editing operation on the source image. This allows LIA-X to better handle large variations between the source and driving data, leading to higher-quality and more controllable portrait animation.

## 5 Experiments

Figure: Figure 3: Sparsity Analysis. We show a comparison of the $\mathcal{A}_{r\rightarrow s}$ activations of two videos between two models - one trained without sparse motion dictionary (top), and the other with a sparse motion dictionary (down). It can be clearly observed that when learned without a sparsity constraint, the model reconstructs each frame by activating nearly all the motion vectors. In contrast, the model trained with a sparsity constraint selects only a few vectors to be activated for each reconstruction.
Refer to caption: x2.png

Figure: (a) (a) Yaw
Refer to caption: x3.png

Figure: (a) (a) Original video
Refer to caption: x6.png

In this section, we will first describe the experimental setup, including implementation details and dataset information. Then, we will present a qualitative analysis on the sparsity, interpretability, and controllability of the proposed LIA-X framework. We will also show comparative results between LIA-X and state-of-the-art methods such as FOMM, TPS, DaGAN, LIA, MCNet, X-Portrait, and LivePortrait, on the task of portrait animation. Finally, we will quantitatively evaluate LIA-X and compare it with the prior work on two important tasks, self-reenactment and cross-reenactment.

Figure: Figure 6: Image Editing. We show image editing capacities of LIA-X. Fine-grained semantic attributes, such as open/close mouth, frown/raise eyebrows, open/close eyes, pout, and smile can be successfully controlled by manipulating the corresponding motion vectors.
Refer to caption: x9.png

Implementation details. We build LIA-X based on the implementation of the original LIA [48, 45]. To scale the model to larger sizes and prevent training instability, we designed novel residual blocks for both the optical flow generator $G_{f}$ and the rendering network $G_{r}$. Our scaling strategy focuses on increasing the number of channels, the depth of residual blocks, as well as using a larger motion dictionary compared to the original LIA. With these architectural enhancements, the largest model size of LIA-X reaches around 1 billion parameters. We train the entire LIA-X model using 8 A100 GPUs, and apply gradient accumulation to increase the effective batch size when training the larger-scale models.

Datasets. To scale the training data, we mix 4 publicly available datasets, i.e., VoxCelebHQ [48], TalkingHead-1KH [40], HDTF [57] and MEAD [37]. Additionally, we include 1 internally collected dataset. In total, our training dataset contains 0.5 million talking head sequences, comprising around 94 million frames and 55,000 different identities. Experiments show that this dataset scaling strategy enables LIA-X to achieve outstanding performance in generalizing to unseen portrait images.

Figure: Figure 7: Qualitative Comparison on Cross-reenactment. LIA-X’s ability to edit the source portrait using interpretable motion vectors before animation allows it to better adjust for initial misalignments with the driving frame. As shown, this editing capability enables LIA-X to significantly outperform other approaches, especially when there are large variations in head pose and facial expression between the source and driving data.
Refer to caption: x10.png

### 5.1 Sparsity Analysis

To demonstrate the effectiveness of our proposed Sparse Motion Dictionary, we visualized the motion coefficient vectors $\mathcal{A}_{r\rightarrow s}$ for two different videos, as shown in Fig. [3](https://arxiv.org/html/2508.09959v1#S5.F3). We compare the results of models with and without using the sparsity constraint in the motion dictionary.

It can be clearly observed that the model without the sparse motion dictionary activates almost all the motion vectors, indicating a lack of selectivity for different input data. The semantics are entangled within each motion vector. In contrast, for the model with the Sparse Motion Dictionary, sparsity can be clearly observed in $\mathcal{A}_{r\rightarrow s}$ - only a few vectors are active, while the contributions of the others can be largely omitted. These results clearly prove the effectiveness of the constraint in improving the sparsity of the motion dictionary.

### 5.2 Interpretability and Controllability Analysis

While we have proven the ability to obtain a Sparse Motion Dictionary, it is crucial to understand whether each vector in the dictionary is interpretable and controllable. To investigate the semantic meanings of the motion vectors, we linearly manipulate the vector $d_{i}$ using:

$$ $\begin{split}z_{s\rightarrow\mathcal{E}(s)}=z_{s\rightarrow s}+a_{i}\mathbf{d_{i}},\end{split}$ (11) $$

where we set $a_{i}$ as a small perturbation ranging from -0.5 to 0.5 with a step of 0.1. Surprisingly, we found that the semantics are well-disentangled in the motion dictionary and can be easily manipulated. Almost all the activated vectors correspond to human-understandable meanings. Fig. [4](https://arxiv.org/html/2508.09959v1#S5.F4) shows examples of LIA-X performing 3D-aware manipulations such asyaw, pitch and roll, without relying on any explicit 3D representation during training or inference.

Image Editing. We further utilize Eq. [11](https://arxiv.org/html/2508.09959v1#S5.E11) to manipulate more motion vectors and demonstrate the results in Fig. [6](https://arxiv.org/html/2508.09959v1#S5.F6). Besides the 3D-aware semantics, LIA-X can control various fine-grained attributes like ‘open/close mouth’, ‘frown/raise eyebrows’, ’open/close eyes’, ‘pout’, ‘smile’, etc. All these interpretable vectors are automatically disentangled through self-supervised learning. With such a powerful editing capability, LIA-X can be readily used as a portrait editing tool, allowing users to linearly combine different semantic operations to achieve complex editing tasks.

**Table 1: Quantitative Evaluation for Self-Reenactment. We compare the performance of LIA-X against state-of-the-art methods on two different resolutions across two datasets.**
| Method | VoxCelebHQ | TalkingHead-1KH |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| L1 $\downarrow$ | LPIPS $\downarrow$ | SSIM $\uparrow$ | PSNR $\uparrow$ | FID $\downarrow$ |  | L1 $\downarrow$ | LPIPS $\downarrow$ | SSIM $\uparrow$ | PSNR $\uparrow$ | FID $\downarrow$ |  |
| $256\times 256$ resolution |  |  |  |  |  |  |  |  |  |  |  |
| FOMM [28] | 0.046 | 0.27 | 0.66 | 22.40 | 12.67 |  | 0.040 | 0.100 | 0.72 | 23.31 | 30.39 |
| DaGAN [16] | 0.044 | 0.110 | 0.69 | 23.04 | 9.13 |  | 0.036 | 0.088 | 0.77 | 24.95 | 25.50 |
| TPS [58] | 0.043 | 0.112 | 0.70 | 23.24 | 10.82 |  | 0.037 | 0.089 | 0.77 | 24.56 | 28.05 |
| MCNet [15] | 0.040 | 0.176 | 0.72 | 23.73 | 18.63 |  | 0.030 | 0.097 | 0.79 | 25.70 | 28.06 |
| LIA-X | 0.036 | 0.095 | 0.73 | 24.82 | 10.74 |  | 0.035 | 0.086 | 0.78 | 26.26 | 24.71 |
|  | $512\times 512$ resolution |  |  |  |  |  |  |  |  |  |  |
| X-Portrait [49] | 0.110 | 0.302 | 0.56 | 16.99 | 19.92 |  | 0.058 | 0.134 | 0.63 | 19.46 | 41.19 |
| LivePortrait [11] | 0.087 | 0.264 | 0.67 | 17.45 | 12.90 |  | 0.052 | 0.120 | 0.73 | 20.26 | 39.98 |
| LIA [48] | 0.052 | 0.211 | 0.68 | 22.14 | 21.86 |  | 0.049 | 0.165 | 0.72 | 23.37 | 44.64 |
| LIA-X | 0.040 | 0.160 | 0.75 | 24.39 | 12.50 |  | 0.035 | 0.115 | 0.80 | 26.07 | 38.93 |

Video Editing. Once we can edit a single portrait, extending the editing to the video level becomes feasible. Given a real-world talking head video, we first apply Eq. [11](https://arxiv.org/html/2508.09959v1#S5.E11) on the initial frame to manipulate the target semantics. Then, we use Eq. [10](https://arxiv.org/html/2508.09959v1#S4.E10) to transfer the motion to the edited first frame. Fig. [5](https://arxiv.org/html/2508.09959v1#S5.F5) demonstrates the results of using LIA-X to rotate the portrait in a video, where the identity is well-preserved while the head pose is seamlessly changed.

**Table 2: Quantitative Evaluation for Cross-Reenactment. We compare the performance of LIA-X against state-of-the-art methods on the constructed dataset for cross-reenactment scenarios.**
| Method | ID Similarity $\downarrow$ | Image Quality $\uparrow$ |
| --- | --- | --- |
| FOMM [28] | 0.262 | 37.08 |
| DaGAN [16] | 0.272 | 39.30 |
| TPS [58] | 0.216 | 38.27 |
| MCNet [15] | 0.252 | 37.88 |
| X-Portrait [49] | 0.217 | 55.41 |
| LivePortrait [11] | 0.243 | 51.41 |
| LIA-X | 0.206 | 58.74 |

Portrait Animation. We also qualitatively compare LIA-X with state-of-the-art methods on the challenging task of cross-identity portrait animation, as shown in Fig. [7](https://arxiv.org/html/2508.09959v1#S5.F7). The visualizations clearly showcase the effectiveness of LIA-X’s editing stage, which can significantly improve the generated results by aligning the source portrait with the initial driving frame. This editing capability allows for a more controllable animation process compared to previous techniques. In contrast, other state-of-the-art methods struggle when the source and driving data have large variations in head poses, facial expressions, and identities. Their performance degrades dramatically in such cross-identity scenarios, whereas LIA-X maintains high-quality and controllable animation results by leveraging its interpretable motion representations and the tailored ”edit-warp-render” pipeline. This comparison highlights a key advantage of our proposed framework - its ability to handle large discrepancies between the source and driving data through the initial editing stage, before seamlessly applying the motion transfer.

**Table 3: Scalability analysis on VoxCelebHQ.**
| Model | L1 $\downarrow$ | LPIPS $\downarrow$ | SSIM $\uparrow$ | PSNR $\uparrow$ |
| --- | --- | --- | --- | --- |
| Base (0.05B) | 0.043 | 0.171 | 0.72 | 23.62 |
| Middle (0.3B) | 0.040 | 0.16 | 0.74 | 24.31 |
| Large (0.9B) | 0.040 | 0.16 | 0.75 | 24.39 |

**Table 4: Scalability analysis on TalkingHead-1KH.**
| Model | L1 $\downarrow$ | LPIPS $\downarrow$ | SSIM $\uparrow$ | PSNR $\uparrow$ |
| --- | --- | --- | --- | --- |
| Base (0.05B) | 0.042 | 0.13 | 0.77 | 24.98 |
| Middle (0.3B) | 0.035 | 0.113 | 0.79 | 25.84 |
| Large (0.9B) | 0.035 | 0.115 | 0.80 | 26.07 |

### 5.3 Quantitative Evaluation

We quantitatively compare LIA-X with state-of-the-art methods on two important tasks, self-reenactment and cross-reenactment. To ensure a fair comparison, we train LIA-X at two different resolutions, $256\times 256$ and $512\times 512$, and compare with the corresponding methods.

Self-Reenactment. We evaluate our method on the validation sets of VoxCelebHQ [48] and TalkingHead-1KH [40], which contain 483 and 25 videos respectively. We reconstruct each video sequence by using the first frame as the source image and the entire video as the driving data. We report results using several metrics, L1, LPIPS, SSIM, PSNR, and FID. As shown in Tab. [1](https://arxiv.org/html/2508.09959v1#S5.T1), on both high and low resolutions, LIA-X outperforms both GAN-based and diffusion-based state-of-the-art methods across all the metrics, demonstrating the effectiveness of our proposed architecture design and training strategy.

Cross-Reenactment. To construct the validation set for cross-reenactment, we select 70 videos from the HDTF dataset [57] as the driving data, and for each video, we randomly select 2 images from the AAHQ dataset [18] as the source data, resulting in a total of 140 videos. Since there is no ground truth data for this task, we evaluate the results using two metrics, Identity Similarity and Image Quality. Identity Similarity measures the average embedding difference between each generated frame and the source portrait, while Image Quality is computed following  [33] to indicate the generated image quality. As shown in Tab. [2](https://arxiv.org/html/2508.09959v1#S5.T2), LIA-X outperforms other methods in both metrics, proving its ability to naturally transfer motion while preserving the original identities.

### 5.4 Ablation study on scalability

To verify the effectiveness of scalability, we conduct an ablation study on the model size. We train three variations of LIA-X with 0.05B, 0.3B, and 0.9B parameters, respectively, while keeping the same training configuration. The three models follow the same architectural design and only differ in the number of residual blocks, channel numbers, and motion dictionary size. The results in Tab. [3](https://arxiv.org/html/2508.09959v1#S5.T3) and  [4](https://arxiv.org/html/2508.09959v1#S5.T4) clearly demonstrate that scaling is effective for improving model performance. However, we note that the improvement from 0.3 billion to 0.9 billion parameters becomes relatively minor. We hypothesize that this could be due to the current dataset size not being large enough to fully support training such a large-scale model. To further push the limits of LIA-X’s scalability and performance, we believe the involvement of even larger and more diverse training datasets would be necessary.

## 6 Limitations and future work

While our proposed method has achieved promising performance, there are still limitations need to be tackled in future work. Firstly, current model can only be used on fixed resolution, designing novel technique to allow dynamic resolution may further improve the performance. Secondly, our model follows the convolutional network architecture which may have limitations to further scale up. DiT-based [25] architecture should be explored in future work for scalability.

## 7 Conclusion

In this work, we have introduced LIA-X, a novel portrait animator that incorporates an interpretable Sparse Motion Dictionary. This enables LIA-X to support a highly controllable ‘edit-warp-render’ animation strategy. Furthermore, we have analyzed the scalability of the LIA-X architecture, demonstrating its ability to achieve outstanding results when scaling up to larger model sizes, up to 1 billion parameters. Extensive evaluations show that LIA-X outperforms SOTA methods across several benchmarks, while also supporting diverse applications like fine-grained image/video editing and high-quality portrait animation. We envision our technique serve as a valuable complement to current generative approaches and provide a novel way for interpretable video generation.

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## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: NA
soundness: 2
strengths: Novelty & Interpretability – Introducing sparsity in the motion dictionary is a simple yet effective idea that yields human-interpretable motion vectors (e.g., yaw, pitch, smile).

Controllability – The edit-warp-render approach is well-motivated and addresses pose/expression misalignment issues common in talking-head animation.
summary: This paper introduces LIA-X, a self-supervised autoencoder for controllable portrait animation that incorporates a Sparse Motion Dictionary to make motion codes interpretable. Unlike prior “warp-render” methods, LIA-X supports an “edit-warp-render” pipeline, enabling users to adjust head pose or facial expression before transferring motion from a driving video. The authors scale the model up to ~1B parameters, demonstrating improved performance and efficiency over GAN and diffusion baselines such as FOMM, LivePortrait, and LIA.

The key difference w.r.t the original LIA is the introduction of a sparsity constraint in the coefficients of motion codes, which results in "semantic" vectors. And the addition of scalability experiments with big networks.
weaknesses: Limited Theoretical Depth – The sparse dictionary idea, though effective, is conceptually straightforward (essentially regularization on motion coefficients). The paper lacks deeper analysis of why sparsity leads to disentanglement.

Evaluation Diversity – While results on self- and cross-reenactment are strong, the experiments are limited to face animation datasets; no user study or generalization to non-portrait domains is shown.

Scalability Ceiling – Gains saturate beyond 0.3B parameters (Tab. 3); the discussion attributes this to data limits but doesn’t provide strong evidence.

Writing – Though generally clear, some sections (especially 3.3 and 4.2) repeat similar ideas; could benefit from tighter exposition and clearer distinction between training and inference stages.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: please check the weakness part.
soundness: 2
strengths: The Sparse Motion Dictionary introduces an interpretable regularization mechanism within the LIA framework. Although sparsity is a classical idea, applying it to disentangle latent motion vectors for human-readable semantics is a novel interpretability-driven adaptation. The edit-warp-render paradigm offers a conceptually clean way to integrate user control into a previously self-supervised latent animation pipeline.
summary: This paper presents LIA-X, an interpretable and controllable portrait animation framework that transfers facial dynamics from a driving video to a static source portrait. The method builds upon LIA by introducing a sparsity constraint on motion coefficients to encourage each motion basis vector in the dictionary to correspond to semantically meaningful and disentangled facial motions (e.g., yaw, pitch, mouth, eyes). Leveraging interpretable latent codes, the model supports fine-grained user-controlled editing before warping and rendering.
weaknesses: 1. The overall architecture (Encoder–Flow–Renderer) and loss formulation are largely inherited from LIA. The paper provides no theoretical justification or analytical evidence explaining why sparsity should induce semantic disentanglement in the motion dictionary. As a result, the contribution is primarily incremental.
2. The claimed “controllability” arises randomly from training statistics and is not guaranteed or reproducible across runs or identities.
3. The model lacks mechanisms (such as mutual information or attribute supervision) to consistently bind dictionary vectors to specific facial semantics. Sparsity alone cannot prevent cross-correlation between motion vectors and different vectors can still encode mixed semantics.
4. Excessive sparsity penalty could reduce motion fidelity, causing stiff or incomplete facial movements; this trade-off is not analyzed.
5. The paper does not discuss how the model performs under challenging cases such as extreme expressions, occlusions, or large head rotations, which are critical for assessing robustness.
6. Despite its interpretability improvements, LIA-X still inherits the visual quality of autoencoder-based methods—limited fidelity, over-smoothing, and inferior texture sharpness—compared to modern diffusion-based portrait animation approaches.
7. L1 regularization could introduce gradient instability in GAN training, but no visualization of training dynamics is given.

---

confidence: 4
contribution: 3
initial_rating: 4
presentation: 3
questions: 1. **Question about Sparse Motion Dictionary:**
    
    - **Architectural Details:** Could you please specify the scale and parameters of the motion dictionary, including the total number and the dimension of the embedding?
        
    - **Quantitative Sparsity:** Besides the visual comparsion on only one video sample, autho(s) should provide quantitative metrics that characterize the sparsity.
        
    - **Properties of the Motion Space:** The paper claims the motion embeddings are disentangled and interpretable. To substantiate this, could author(s) evaluate the interpolation properties of the learned motion space? For example, a comparison of the perceptual path length (PPL) in the motion space, with and without the sparsity constraint, would provide strong evidence for this claim.
        
2. **Question about Experiments:**
    
    - **User study:** Could author(s) conduct a user study to compare your method against baselines? This would provide a more robust measure of perceptual quality than automated metrics alone.
        
    - **Generalization:** Could author(s) provide qualitative examples of the model's performance on non-human characters (e.g., cartoons, avatars) to demonstrate its generalization capabilities?
        
3. **Question about Experiments Model Architecture and Design Rationale:**
    
    - **Architectural:** The paper mentions that the architecture is inspired by StyleGAN-T. For reproducibility, could author(s) provide more specific details about the encoder, generator, and discriminator architectures?
        
    - **Rationale for Model Selection:** Could author(s) offer more insight into the decision to use a GAN-based framework, especially given the well-documented training stability advantages of modern diffusion models? Furthermore, have author(s) considered whether the learned motion dictionary could be used to fine-tune a pre-trained video diffusion model to potentially achieve superior portrait animation performance?
soundness: 3
strengths: 1. **Novel Synthesis of Techniques:** The paper presents an elegant integration of two powerful and established methods: GAN and sparse dictionary learning, applying them effectively to the portrait animation domain.
    
2. **Demonstrated Scalability:** The experimental results successfully demonstrate the architecture's capacity to scale to high-resolution outputs and handle complex animation tasks.
summary: This paper targets the task of portrait animation, proposing a GAN-based autoencoder framework. A key contribution is the integration of a sparse motion dictionary, which is trained concurrently with the generative model to learn a disentangled representation of facial motion. Leveraging this motion dictionary, the paper introduces an "edit-warp-render" strategy to effectively address pose misalignment in cross-identity reenactment. Extensive experiments are presented to demonstrate the scalability of the proposed method.
weaknesses: **Insufficient Ablation of the Sparse Motion Dictionary:** The sparse motion dictionary is central to the paper's contributions, yet it is not sufficiently validated through detailed ablation studies. The specific benefits of the dictionary and its sparsity constraint are therefore not fully quantified.