Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping

## Abstract

Abstract. The ability to reconstruct realistic and controllable upper body avatars from casual monocular videos is critical for various applications in communication and entertainment. By equipping the most recent 3D Gaussian Splatting representation with head 3D morphable models (3DMM), existing methods manage to create head avatars with high fidelity. However, most existing methods only reconstruct a head without the body,
substantially limiting their application scenarios.
We found that naively applying Gaussians to model the clothed chest and shoulders tends to result in blurry reconstruction and noisy floaters under novel poses. This is because of the fundamental limitation of Gaussians and point clouds – each Gaussian or point can only have a single directional radiance without spatial variance,
therefore an unnecessarily large number of them is required to represent complicated spatially varying texture, even for simple geometry.
In contrast, we propose to model the body part with a neural texture that consists of coarse and pose-dependent fine colors. To properly render the body texture for each view and pose without accurate geometry nor UV mapping, we optimize another sparse set of Gaussians as anchors that constrain the neural warping field that maps image plane coordinates to the texture space. We demonstrate that Gaussian Head & Shoulders can fit the high-frequency details on the clothed upper body with high fidelity and potentially improve the accuracy and fidelity of the head region. We evaluate our method with casual phone-captured and internet videos and show our method archives superior reconstruction quality and robustness in both self and cross reenactment tasks. To fully utilize the efficient rendering speed of Gaussian splatting, we additionally propose an accelerated inference method of our trained model without Multi-Layer Perceptron (MLP) queries and reach a stable rendering speed of around 130 FPS for any subjects.

## 1. Introduction

Personalized and controllable 3D head avatar is a crucial asset for interactive Mixed Reality and metaverse applications.
Recent developments in the 3D representations such as 3DMM (Li et al., ; Gerig et al., ), Neural Radiance Field (Mildenhall et al., ), Instant Neural Primitives (Müller et al., ), and other implicit representations (Mescheder et al., ) have brought
rapid advancements in the reconstruction of vivid and controllable neural avatars (Zheng et al., ; Grassal et al., ; Zielonka et al., ; Gao et al., ).
With the most recent 3D Gaussian Splatting representation (Kerbl et al., ), neural avatars can be convincingly
reconstructed from a monocular video with impressive fidelity.
However, most current methods for creating head avatars concentrate solely on the face and head, discarding other visible parts of the body by using a semantic mask during the training process.
Consequently, this results in avatars that appear as heads without bodies, which is not sufficient
for many immersive applications, including video conferencing, where a more complete avatar is needed (Shao et al., ; Xiang et al., ; Zielonka et al., ; Gao et al., ).
Recent techniques aim to create more complete avatars by including visible parts of the body, like shoulders and chest (Zheng et al., ; Zhao et al., ; Wang et al., ; Zheng et al., ). However, they are limited to simplified settings where the subject dresses in plain clothing without detailed textures and is instructed to restrict upper body movement. On the other hand, existing full-body avatar methods typically focus on the overall quality of the limbs and torso and fail to faithfully capture the fine details such as high-frequency texture on clothes (Kocabas et al., ; Hu et al., ; Li et al., ; Lei et al., ).
Applications of neural avatars that require detailed reconstruction of the upper body area often encounter significant challenges in capturing faithful and intricate details. Overall, current methods still fall short of delivering the level of performance needed for practical, real-world use.

The Gaussian Splatting representation, while being efficient and effective in certain aspects, struggles with accurate modeling of clothed upper bodies. As one of its fundamental limitations, each Gaussian can represent only one color from a specific viewing angle. This heavily limits its capability to handle dynamic objects that have complex textures, such as clothing with intricate patterns. To capture the detailed appearance of such objects, an excessively large number of Gaussians would be needed, increasing memory requirement and slowing down the rendering speed.
In addition, the complicated pose-dependent appearances such as brightness changes and cloth wrinkles further increase the difficulty of modeling them with plain Gaussians alone.
As a result, when the reconstructed avatar is driven to novel poses, the Gaussians tend to produce several undesirable artifacts such as blurred texture, incorrect colors or floating ellipsoid; see Fig 1.

To address the limitations of existing Gaussian-based avatar methods on clothed upper-body region, we argue that the chest and shoulders are expected to have relatively simpler geometry and more intricate deformation compared to the head.
Therefore, modeling them with regular and 3DMM-driven Gaussians would be unsuitable and is an over-complication of the problem.
Instead, a more appropriate and standard
approach would be representing their appearance with a high-frequency texture.

In a traditional texture-based rendering pipeline, the texture is first mapped to mesh geometry in the 3D world space via UV mapping, and then rasterized to the 2D image plane in the view space to obtain the pixel color. However, this approach requires a well-defined UV mapping and accurate mesh geometry, which is challenging to obtain from monocular videos alone due to the lack of multi-view correspondences.
Besides, compared to faces that share more common characteristics and stronger priors, the appearance of upper bodies can vary dramatically depending on the texture and tightness of the clothes and they hence contain
fewer detectable landmarks.
Consequently, body 3DMMs such as SMPL (Loper et al., ) fail to provide geometry accurate enough for this purpose.

Hence, we propose to bypass the mapping from texture space to world space, and instead use a sparse set of Gaussians as “anchors” to define a direct neural warping field between the texture space and the image plane. As the tracking of body 3DMM tends to be inaccurate due to the lack of landmarks, we only transform anchor Gaussians together with the head Gaussians via a head FLAME 3DMM (Li et al., ) through Linear Blend Skinning (LBS). The transformed anchor Gaussians are used as soft constraints of the texture warping represented by a coordinate-based MLP, which is optimized together with the neural texture, regular Gaussians, and the anchor Gaussians. As the resolution of the neural texture is not limited by the number of Gaussians or the density control scheme, we can easily learn the high-frequency textures with sharp details on the clothes and avoid the common artifacts exhibited in Gaussian rendering under novel poses; see Fig 1.

To maintain a competitive rendering speed with Gaussian Splatting and
enable real-time interactive applications, we additionally propose a method to remove the neural warping field and neural texture in the model and allow inference of reconstructed avatars at novel poses without any MLP queries. This accelerated inference effectively increases the rendering speed from 70 FPS to around 130 FPS, which surpasses the rendering speed of plain Gaussian Splatting avatars for subjects with high-frequency clothes.

We evaluate the proposed method with various casual monocular videos collected using smartphones or from the Internet.
Compared to state-of-the-art methods which incorporate different representations including neural radiance field, Gaussian Splatting, and point clouds, we show that our approach achieves better performance and robustness for both self-reenactment and cross-reenactment tasks.
In summary, our contributions are:

- •
We propose a novel approach that maps intricate texture to the image plane via a sparse set of anchor Gaussians driven by LBS with 3DMM. This allows accurate and robust modeling of high-fidelity clothed chest and shoulders with less number of Gaussians.
- •
We propose a method to remove the MLP in our method at inference time to prevent any costly queries when rendering with novel poses and expressions and reach a rendering speed of around 130 FPS.

## 2. Related Works

#### Neural Head Avatars

The recent advancement in neural 3D implicit and explicit representations has sparked a surge of methodologies within the field of controllable 3D head avatars. Among these approaches, a prominent family of methods involves the reconstruction of a 5D neural radiance field, manifested through various forms such as pure MLP (Gafni et al., ; Wang et al., ; Kirschstein et al., ), hash grid latents (Xu et al., ; Gao et al., ; Zielonka et al., ; Xu et al., ; Dhamo et al., ; Xiang et al., ; Saito et al., ; Chen et al., ), and 3D Gaussians (Wang et al., ; Zhao et al., ). Another set of methods utilizes more explicit representations such as deformable meshes with neural textures  (Grassal et al., ; Zheng et al., ; Buehler et al., ; Gropp et al., ; Khakhulin et al., ; Kim et al., ) and point clouds (Zheng et al., ).
Notably, the point cloud representation used by PointAvatar (Zheng et al., ) is similar
to 3D Gaussian Splatting in
their modeling of point size, individual point colors, and opacities that decay with the distance to point center.
The core differences between the two are that Gaussian Splatting additionally supports anisotropic Gaussians, individual Gaussian size, view-dependent appearance via SH coefficients, and a more sophisticated density control scheme that grows the number of Gaussians.
With the most recent Gaussian Splatting techniques, the head avatars reconstructed from monocular videos already reach high fidelities.
However, many methods simplify the problem by reconstructing only the head and neck part, resulting in a head-only reconstruction that is not suitable for many applications.
Several methods have attempted to also model the chest and shoulders to provide a more immersive user experience (Zheng et al., ; Zhao et al., ; Wang et al., ; Zheng et al., ). However, they are limited to simple clothes with plain colors, and cannot handle the movements in the upper body in the video.

#### Neural Full-Body Avatars

Several works have tried to reconstruct a controllable full-body neural avatar from multi-view or monocular videos (Liu et al., ; Shao et al., ; Svitov et al., ; Li et al., ; Lei et al., ; Kocabas et al., ; Hu et al., ; Jiang et al., ).
Due to the highly articulated nature of human bodies, they tightly rely on body 3DMMs to deform the neural body representation via LBS.
However, they typically fail to faithfully capture subjects with complicated or loose clothing as those cannot be modeled with existing body 3DMMs.
Li et. al. (Li et al., ) first reconstruct an SDF field as parametric models, then incorporate a StyleUnet to directly infer parameters of 3D Gaussians in the canonical space and deform it to each pose via LBS. They can reconstruct high-quality avatars even under cases of loose clothing, but require synchronized multiview videos as input.
As methods that reconstruct animatable full-body avatars typically focus on the overall quality of the torso and limbs, they tend to present non-trivial artifacts when reconstructing and re-animating an avatar that has a tight focus around the head and shoulder regions.

## 3. Method

Figure: Figure 2. Method. (a) We utilize a set of standard head Gaussians and anchor Gaussians driven by LBS with the FLAME model. (b) Anchor Gaussians are initialized with a set of corresponding target coordinates in the texture space. This 3D-2D correspondence is used to constrain (c) a neural texture warping field that maps each pixel on the image plane $\mathbf{x}_{v}$ to a pixel in the texture space $\mathbf{x}_{t}$. (d) We then sample in the texture space to fetch the coarse texture $\mathbf{T}_{c}$ and latent texture $\mathbf{T}_{f}$, which is parsed by an MLP to obtain pose-dependent fine texture $\mathbf{C}_{f}^{t}$. Both coarse and fine textures are then combined to form a body texture, which is blended with other Gaussians through alpha compositing to form the final rendering.
Refer to caption: extracted/2405.12069v2/figures/method.png

Given a monocular video featuring a talking subject with various expressions and head poses, our goal is to reconstruct a high-fidelity and animatable avatar including the head and clothed upper body.
As illustrated in Fig 2, our method jointly optimizes 1) a set of standard 3D Gaussians (Kerbl et al., ) which tightly follow the transformation of 3DMM via LBS to represent the head region, 2) a set of sparse anchor Gaussians spawning over the clothed body, and 3) a learnable neural texture with pose-dependent neural texture warping field constrained by the anchor Gaussians to represent the clothed body with sharp details and high robustness.

### 3.1. Preliminary- Gaussian Splatting

3D Gaussian Splatting is a volumetric representation that utilizes a dense set of anisotropic Gaussians with varying opacity and view-dependent radiance to represent 3D geometry and appearance. Each Gaussian is described with four parameters: position (Gaussian mean) $\bm{\mu}$, 3D covariance matrix $\bm{\Sigma}$, opacity $\alpha$ and Spherical Harmonic (SH) coefficients $\mathbf{SH}$ for computing view-dependent RGB color. For ease of optimization, the covariance matrix is further decomposed into a scaling matrix $\mathbf{S}$, stored as a scaling vector $\mathbf{s}$, and a rotation matrix $\mathbf{R}$, stored as a quaternion vector $\mathbf{q}$. The covariance matrix is obtained as: $\bm{\Sigma}=\mathbf{RS}\mathbf{S}^{T}\mathbf{R}^{T}$.

To render 3D Gaussians to RGB images, their means are projected onto 2D image plane with standard projective transformation, while the projected covariance matrix is obtained by $\bm{\Sigma}^{\prime}=\bm{JW\Sigma W}^{T}\bm{J}^{T}$, where $\mathbf{W}$ is the world to camera transformation and $\mathbf{J}$ is the Jacobian approximating the projective transformation (Zwicker et al., ). The rendered RGB color at each pixel is then obtained through:

$$ (1) $\displaystyle\mathbf{C}(\mathbf{x})$ $\displaystyle=\sum_{i\in N}\mathbf{c}_{i}\alpha^{*}_{i}(\mathbf{x})\prod_{j=1} ^{i-1}(1-\alpha^{*}_{j}(\mathbf{x})),$ (2) $\displaystyle\alpha^{*}_{i}(\mathbf{x})$ $\displaystyle=\alpha_{i}\exp\left(-\frac{1}{2}(\mathbf{x}-\bm{\mu}^{\prime}_{i })^{T}\bm{\Sigma}^{\prime-1}(\mathbf{x}-\bm{\mu}^{\prime}_{i})\right),$ $$

where $\mathbf{x}$ is the 2D pixel coordinate, $\mathbf{c}_{i}$ is the view-dependent RGB radiance of i-th Gaussian on the ray obtained from SH function, $\alpha_{i}$ and $\bm{\mu}^{\prime}_{i}$ are the opacity and projected 2D mean of the i-th Gaussian respectively.

### 3.2. FLAME-Driven Head Gaussians

As the face region contains highly distinguishable characteristics and can be described accurately with parametric head 3DMM such as FLAME (Li et al., ), we directly utilize standard 3D Gaussians that are deformed with parametric 3DMM via neural LBS to represent the head part (Zheng et al., ; Zhao et al., ). Specifically, we learn personalized FLAME expression and pose blendshapes and LBS weights through a small 3D coordinate-based MLP for each Gaussian:

$$ (3) $\displaystyle\bm{\mathcal{E}},\bm{\mathcal{P}},\bm{\mathcal{W}}=\operatorname{ MLP}_{d}(\bm{\mu}),$ $$

where $\bm{\mathcal{E}}\in\mathbb{R}^{n_{e}\times 3}$ are the expression blendshapes, $\bm{\mathcal{P}}\in\mathbb{R}^{n_{p}\times 9\times 3}$ are the pose blendshapes, $\bm{\mathcal{W}}\in\mathbb{R}^{n_{j}}$ are the LBS weights corresponding to each of the $n_{j}$ bones.
Following (Hu and Liu, ),
we use the standard skinning function LBS to obtain the rotation $\bm{R}$ and translation $\bm{T}$ for each Gaussian, and apply them to get the Gaussian mean $\bm{\mu}^{d}$ and covariance $\bm{\Sigma}^{d}$ in the 3D view space:

$$ (4) $\displaystyle\bm{R},\bm{T}$ $\displaystyle=\textsc{LBS}(\bm{B}_{\bm{\mathcal{P}}}(\theta;\bm{\mathcal{P}})+ \bm{B}_{\bm{\mathcal{E}}}(\psi;\bm{\mathcal{E}}),\mathbf{J}(\psi),\theta,\bm{ \mathcal{W}}),$ (5) $\displaystyle\bm{\mu}^{d}$ $\displaystyle=\bm{R}\bm{\mu}+\bm{T},$ (6) $\displaystyle\bm{\Sigma}^{d}$ $\displaystyle=\bm{R}\bm{\Sigma}\bm{R}^{T},$ $$

where $\mathbf{J}$ is the joint regressor in FLAME, and $\bm{B}_{\bm{\mathcal{P}}}$ and $\bm{B}_{\bm{\mathcal{E}}}$ are linear combination of blendshapes based on per-frame coefficients $\theta$ and $\psi$ that control the head animation.
They can then be rendered with a standard Gaussian rasterization pipeline in Eq 1.

### 3.3. 3D-2D Correspondence via Anchor Gaussians

3D Gaussian Splatting has shown promising performance and robustness in reconstructing 3D geometry and appearance from RGB images. However, they suffer from a significant constraint – each individual Gaussian can only represent a spatially invariant color under a fixed viewing direction, hence a vast number of Gaussians is required to represent objects with detailed textures, regardless of the actual complexity of the geometry.
A naive application of Gaussian Splatting therefore fails to capture the fine details of the upper body with complex textures and intricate deformation, and results in blurry details and floating artifacts under challenging poses.

We hence propose to learn a high-frequency texture in canonical texture space, and use a sparse set of Gaussians as anchors to guide the warping between texture space and image plane.
As such,
we only need a small number of Gaussians and a texture with per-pose warping to represent a clothed body with arbitrarily complicated textures.
Since anchor Gaussians themselves do not need to exactly represent the high-frequency appearance, we can model them as a simplified version of regular Gaussians:
they only use view-independent RGB colors, are isotropic Gaussians with quaternion fixed at $(1,0,0,0)$, and are excluded from the density control and therefore are not split, cloned, or pruned.
To prevent them from becoming trivial in rendering, their opacity and size are clamped to be no smaller than $0.05$ and $0.0001$ respectively.

The anchor Gaussians are initialized as follows: after a short warm-up period that only trains plain Gaussian, we first reproject all Gaussian means onto the image plane of a canonical training frame, and filter out Gaussians that are located around the head region based on semantic masks.
We then use farthest point sampling (Qi et al., ) to select $N_{a}=1024$ Gaussians as anchor Gaussians. The first SH basis is converted to RGB values and the anchor scales in three directions are averaged to form a single scale for the anchor Gaussians.
We then obtain a sparse set of anchor Gaussians, as well as their projected 2D means $\hat{\mathbf{x}}^{v}_{i}$ on the image plane (2D view space) of the canonical frame:

$$ (7) $\displaystyle\hat{\mathbf{x}}^{v}_{i}$ $\displaystyle=\mathbf{P}(\hat{\bm{\mu}}^{d}_{i}),$ $$

where
$\mathbf{P}$ is the camera projective transformation, $\hat{\bm{\mu}}_{i}^{d}$ is the 3D Gaussian mean of the $i$-th anchor Gaussian transformed to 3D view space with LBS.
To build the correspondence between anchor Gaussians and texture space coordinates, we assume that the mapping between the 2D image plane of the canonical frame and the texture space is an identity mapping. We can hence define a function $f_{anchor}(i)$ as a fixed correspondence between the $i$-th 3D anchor Gaussian mean and its target 2D pixel coordinate in texture space:

$$ (8) $\displaystyle f_{anchor}(i):=\mathbf{I}(\hat{\mathbf{x}}^{v}_{i}),$ $$

where $\mathbf{I}$ is the identity function to map 2D image plane coordinates to texture space. Note that $f_{anchor}(i)$ is fixed after initialization and does not update with further optimization of $\hat{\bm{\mu}}_{i}$. Such correspondences will later be used to constrain the pose-dependent texture warping, as will be detailed in Sec 3.6.

### 3.4. Neural Texture and Texture Warping

We use a trainable neural texture in canonical space with a pose-dependent neural texture warping field to represent the part of the avatar with relatively simple overall geometry and complicated appearances, i.e., the clothed shoulder and chest.
In a traditional textured mesh rendering pipeline, the texture is first mapped to the mesh triangles through a pre-defined UV mapping, and the meshes are then rasterized to find the first intersections with the camera rays. Those first intersections therefore establish a mapping between texture space and image plane. However, this approach is not applicable without accurate surfaces and well-defined UV mapping. We instead propose to bypass the intermediate step and learn a per-pose warping that directly maps pixel coordinates on image plane $\mathbf{x}_{v}$ to the texture coordinates $\mathbf{x}_{t}$ for texture fetching. Specifically, the warping field is represented using a coordinate-based MLP:

$$ (9) $\displaystyle\Delta_{\mathbf{x}}=\operatorname{MLP}_{w}\left(\gamma(\mathbf{x} _{v}),\gamma(\theta),\gamma(\mathbf{t}),\gamma(\mathbf{x}_{ldmk})\right),$ $$

where
$\gamma$ is the positional encoding (Mildenhall et al., ),
$\mathbf{\theta}$ is the FLAME pose parameters containing head and neck rotations, $\mathbf{t}$ is the camera position, $\mathbf{x}_{ldmk}$ is 2D body landmarks for neck, left and right shoulders.
The corresponding texture coordinate is obtained as $\mathbf{x}_{t}=\mathbf{x}_{v}+\Delta_{\mathbf{x}}$.

Our optimizable texture includes a coarse texture $\mathbf{T}_{c}$ with 3 channels and a latent texture $\mathbf{T}_{f}$ with $D_{t}$ channels. Both textures have sizes of $[H+2P,W+2P]$, where $H,W$ are the image height and width, $P$ is the padding size which we empirically set to 50 to account for body parts that move in and out in the video sequence.
The latent texture $\mathbf{T}_{f}$ is passed to an MLP to obtain pose-dependent appearances such as brightness changes on the clothes:

$$ (10) $\displaystyle\mathbf{C}^{t}_{f}(\mathbf{x}_{t})$ $\displaystyle=\operatorname{MLP}_{f}\left(\mathbf{T}_{f}(\mathbf{x}_{t}), \gamma(\theta),\gamma(\mathbf{t}),\gamma(\mathbf{x}_{ldmk})\right),$ $$

where $\mathbf{T}_{c}(\mathbf{x}_{t}),\mathbf{T}_{f}(\mathbf{x}_{t})$ are coarse and latent texture sampled at 2D coordinate $\mathbf{x}_{t}$ via bilinear interpolation.
The textured pixel color at the coordinate $\mathbf{x}_{v}$ is therefore obtained as:

$$ (11) $\displaystyle\mathbf{C}^{t}(\mathbf{x}_{v})$ $\displaystyle=\mathbf{T}_{c}(\mathbf{x}_{t})+\mathbf{C}^{t}_{f}(\mathbf{x}_{t}).$ $$

### 3.5. Rendering

To this end, we have a hybrid representation that includes 3D regular Gaussians that represent the head of the avatar, 3D anchor Gaussians that sparsely span over the body region, and a 2D neural texture for the body. To render all of them together for joint optimization, we simply use alpha blending:

$$ (12) $\displaystyle\mathbf{C}^{*}(\mathbf{x}_{v})$ $\displaystyle=\underbrace{\hat{\mathbf{C}}(\mathbf{x}_{v})}_{\text{Anchor Gaussians}}+\underbrace{(1-\hat{\alpha}(\mathbf{x}_{v}))\mathbf{C}(\mathbf{x}_ {v})}_{\text{Head Gaussians}}$ $\displaystyle+\underbrace{(1-\hat{\alpha}(\mathbf{x}_{v}))(1-\alpha(\mathbf{x} _{v}))\mathbf{C}^{t}(\mathbf{x}_{v})}_{\text{Body Texture}},$ $$

where $\hat{\mathbf{C}}(\mathbf{x}_{v}),\mathbf{C}(\mathbf{x}_{v})$ are the rendered RGB color of anchor Gaussian and regular Gaussian, $\hat{\alpha}(\mathbf{x}_{v}),\alpha(\mathbf{x}_{v})$ are the total alpha of anchor Gaussian and regular Gaussian at pixel $\mathbf{x}_{v}$ respectively.

Note that our rendering process always renders anchor Gaussians in front of the regular Gaussians regardless of their actual positions.
Though not physically realistic,
we designed this rendering order so anchor Gaussians are always non-trivial and never occluded by regular Gaussians.

### 3.6. Optimization

The optimization is split into three different stages: anchor warm-up stage, main optimization stage, and texture refinement stage. In the anchor warm-up stage, neither anchor Gaussians nor body texture is applied, only the regular Gaussians are rendered and optimized.
The purpose of this stage is to move Gaussians to
roughly spawn over
the area of interest including both head and body.
At the end of this stage, we initialize anchor Gaussians from regular Gaussians using the method described in Sec 3.3.
In the second stage, we render all of the regular Gaussians, anchor Gaussians, and the textured body with alpha compositing described in Eq 12 and jointly optimize them together. In the last stage, to recover faithful appearance for the body texture and enhance its robustness under novel poses, we remove anchor Gaussians from the rendering pipeline, i.e., we set $\hat{\mathbf{C}}$ and $\hat{\alpha}$ to 0 0 in Eq 12., and freeze everything else except for the neural texture, texture warping field, and opacity and SH of regular Gaussians.

Following  (Zheng et al., , ), the training losses include standard MSE RGB loss $\mathcal{L}_{\bm{C}}=MSE(\mathbf{C}^{*}-\mathbf{C}^{GT})$, and a FLAME regularization that encourages the FLAME blendshapes and LBS weights predicted for each Gaussian stay close to the pseudo ground truth $\tilde{\bm{\mathcal{E}}}_{i},\tilde{\bm{\mathcal{P}}}_{i},\tilde{\bm{\mathcal{
W}}}_{i}$ obtained from the nearest FLAME vertex:

$$ (13) $\displaystyle\mathcal{L}_{flame}$ $\displaystyle=\frac{1}{N}\sum_{i=1}^{N+N_{a}}(\lambda_{\bm{\mathcal{E}}}|\bm{ \mathcal{E}}_{i}-\tilde{\bm{\mathcal{E}}}_{i}|_{2}$ $\displaystyle+\lambda_{\bm{\mathcal{P}}}|\bm{\mathcal{P}}_{i}-\tilde{\bm{ \mathcal{P}}}_{i}|_{2}+\lambda_{\bm{\mathcal{W}}}|\bm{\mathcal{W}}_{i}-\tilde{ \bm{\mathcal{W}}}_{i}|_{2}).$ $$

During main optimization stage, we additionally include a VGG feature loss (Johnson et al., ; Simonyan and Zisserman, ) $\mathcal{L}_{VGG}=|\mathbf{F}_{vgg}(\mathbf{C})-\mathbf{F}_{vgg}(\mathbf{C}^{
GT})|$, and a head mask regularization to encourage regular Gaussians to stay only within the head region and allow the body texture to be trained properly without being occluded:

$$ (14) $\displaystyle\mathcal{L}_{head}=MSE(max(\alpha-\alpha_{head},0)),$ $$

where $\alpha_{head}$ is the alpha mask of the head region obtained with matting pre-processing and semantic mask.
We also include an L1 regularization on the 2D neural warping field to encourage a clean background to be learned in the neural texture, as well as an L1 loss to slowly decrease the opacity of anchor Gaussians to allow the body texture to be trained properly:

$$ (15) $\displaystyle\mathcal{L}_{warp}$ $\displaystyle=\frac{1}{HW}\sum_{i=1}^{HW}|\Delta_{\mathbf{x}_{i}}|,$ (16) $\displaystyle\mathcal{L}_{\hat{\alpha}}$ $\displaystyle=\frac{1}{N_{a}}\sum_{i=1}^{N_{a}}|\hat{\alpha}_{i}|.$ $$

Finally, we include an anchor loss as a soft constraint of the per-pose texture warping:

$$ (17) $\displaystyle\mathcal{L}_{anchor}$ $\displaystyle=\frac{1}{N_{a}}\sum_{i=1}^{N_{a}}(f_{anchor}(i)-(\hat{\bm{x}}^{v }_{i}+\Delta_{\hat{\bm{x}}^{v}_{i}}))^{2},$ $$

i.e., for each anchor Gaussian, we first transform it to 3D view space via LBS, and then project it onto the image plane to obtain its 2D mean $\hat{\bm{x}}^{v}_{i}$ via Eq 7. $\hat{\bm{x}}^{v}_{i}$ is then warped by the neural warping field $\operatorname{MLP}_{w}$ to obtain the corresponding coordinate in the texture space, which is optimized to match the anchor correspondence defined during initialization.

In the third stage, we remove the regularization losses including $\mathcal{L}_{head},\mathcal{L}_{warp}$ and $\mathcal{L}_{\hat{\alpha}}$.

The total training objectives for each of the three stages are as follows:

$$ (18) $\displaystyle\mathcal{L}_{1}$ $\displaystyle=\mathcal{L}_{\bm{C}}+\mathcal{L}_{flame},$ $\displaystyle\mathcal{L}_{2}$ $\displaystyle=\mathcal{L}_{1}+\lambda_{VGG}\mathcal{L}_{VGG}+\lambda_{head} \mathcal{L}_{head}$ (19) $\displaystyle\quad+\lambda_{warp}\mathcal{L}_{warp}+\lambda_{\hat{\alpha}} \mathcal{L}_{\hat{\alpha}}+\lambda_{anchor}\mathcal{L}_{anchor},$ (20) $\displaystyle\mathcal{L}_{3}$ $\displaystyle=\mathcal{L}_{1}+\lambda_{VGG}\mathcal{L}_{VGG}+\lambda_{anchor} \mathcal{L}_{anchor}.$ $$

### 3.7. Accelerated Rendering with No MLP Queries

One of the main advantages of Gaussian Splatting is its highly efficient rendering speed, which enables many real-time and interactive applications. To take full use of this advantage, we propose an accelerated version of our method that requires no MLP queries at inference time. Specifically, after training the model, we first cache the output of $\operatorname{MLP}_{d}$ for all head Gaussians and anchor Gaussians, then cache the view-dependent fine texture by querying the fine texture MLP $\operatorname{MLP}_{f}$ conditioned on the same canonical training frame which was previously used to initialize the anchor Gaussians. The queried fine texture colors are added to the coarse color to make a non-neural RGB texture. To deal with potential noise created by the fine texture MLP at the corners of the texture, we use an off-the-shelf background segmentation network (Chen et al., ) to compute a coarse mask and clean all the pixels outside of the mask; we show the necessity of this step in the supplementary.
To replace the neural warping field $\operatorname{MLP}_{w}$ that warps image plane coordinates to texture space, we rely on the correspondence between anchor Gaussians and texture space coordinates to estimate a homography at inference time.
Specifically, we first project all anchor Gaussians to the image plane of the canonical training frame, and then remove any anchor Gausians that go beyond the view frustum.
To deal with any potential discrepancy between the neural warping field and the anchor correspondences, we update those correspondences based on the prediction of the neural warping field on the current frame:

$$ (21) $\displaystyle f_{anchor}(i):=\hat{\mathbf{x}}_{v}^{i}+\Delta_{\hat{\mathbf{x}} _{v}^{i}}.$ $$

After that, we randomly select 100 training frames and use RANSAC (Fischler and Bolles, ) to estimate a homography between the image plane coordinates of anchor Gaussians and their corresponding texture space coordinates, and remove anchor Gaussians that are considered outliers by RANSAC. This effectively removes any anchor deformation that cannot be described by the rigid transformation.
Finally, at inference time, we perform LBS on regular head Gaussians and anchor Gaussians. Based on the image plane coordinates of the anchor Gaussians $\hat{\mathbf{x}}_{v}^{i}$ and their correspondences $f_{anchor}$, we compute a homography with the least square error via singular value decomposition. The estimated transformation is applied to all pixels on the image plane to find the corresponding non-neural texture, which is then blended with the head Gaussians to form the final rendering.
This accelerated inference approach effectively increases the rendering speed from around 70 FPS to 130 FPS.

## 4. Evaluation

Table: Table 1. Quatitative evaluation of self-reenactment task We report PSNR$\uparrow$, SSIM$\uparrow$, and LPIPS$\downarrow$ ,and color the best and second-best methods for each subject respectively. Our full method achieves much better performance compared to existing baselines. While Ours (No MLP) sometimes achieves slightly lower PSNR, which is known to be over-sensitive to small misalignments and prefer blurry results (Park et al., ), we show it achieves better LPIPS than existing methods.

### 4.1. Datasets

We evaluate different methods on 1 mobile phone sequence from PointAvatar (Zheng et al., ), 2 internet sequences from Head2Head dataset (Koujan et al., ), and 4 sequences captured with mobile phones. All sequences are preprocessed with DECA (Feng et al., ) and a slightly modified landmark fitting process from IMAvatar (Zheng et al., ). Additionally, we use DWPose (Yang et al., ) to predict 2D landmarks for nose, neck and shoulders, which are then smoothed with One Euro Filter (Casiez et al., ).

### 4.2. Baselines

We compare our method with four neural head avatar methods based on various representations, including (1) INSTA (Zielonka et al., ), which employs a latent hash grid (Müller et al., ) combined with NeRF (Mildenhall et al., ), (2) PointAvatar (Zheng et al., ), which is based on isotropic point clouds, (3) SplattingAvatar (Shao et al., ), which utilizes Gaussian Splatting attached to local space of 3DMM meshes, and (4) GS*, a baseline we implemented by changing the point cloud representation in PointAvatar to Gaussian Splatting, which is similarly deformed via neural LBS.

### 4.3. Self-Reenactment

We show the quantitative and qualitative results of the self-reenactment task in Tab 4 and Fig 3. The subjects 001 to 007 are shown in order in Fig 3.
Our full version demonstrates superior reconstruction performance compared to existing baselines, especially for subjects with intricate cloth textures.
Our No MLP version does not consistently achieve better PSNR when compared to existing baselines, as it is unable to render pose-dependent appearance changes and intricate cloth deformation. However, we note that it consistently achieves better LPIPS, demonstrating that our No MLP version can still generate realistic and faithful renderings.
This discrepancy among different metrics arises because of the high sensitivity of PSNR to small misalignments in the cloth texture (Park et al., ).
As a result, PSNR tends to prefer blurry reconstruction over sharp but slightly misaligned results.
The qualitative evaluation in Fig 3 demonstrates that both versions of our method are capable of learning sharper and more robust body texture compared to existing methods.
Specifically, INSTA (Zielonka et al., ) and SplattingAvatar (Shao et al., ) fail to learn sensible reconstruction for the body part, as they originally only aim for the reconstruction of head and neck without any body; PointAvatar (Zheng et al., ) learns robust head and body avatars, but produces highly blurred results; GS* learns sensible reconstruction with accurate expression and pose control, but it still misses some sharp textures and can cause floating artifacts and blurry reconstruction under extreme poses. In comparison, our methods can produce much sharper and more reliable rendering under any texture and pose. Please refer to the video results in the Supplementary for additional comparisons.

### 4.4. Cross-Reenactment

For the cross-identity reenactment task, we render the reconstruction of the original identity with FLAME expressions and poses from the source subject. As the subjects in different videos have various crop sizes and distances to the camera, we do not directly take the body landmarks of source subjects, but instead apply the offsets between current landmarks and the landmarks at the canonical frame of the source subject to the landmarks of original identity.
With the full version of our method, we apply an additional Euclidean transformation after warping the image plane coordinates with the MLP.
This is needed to ensure the body texture is always aligned with the head Gaussians under novel poses; see Fig 6.
The Euclidean transformation is simply determined by fitting the MLP warped image plane coordinates of the anchor Gaussians and their target coordinates in the texture space.
To deal with potential artifacts caused by coordinates warped to unseen corner parts in the texture, we apply the same appearance distillation process and remove the fine texture MLP.
The No MLP version is applied the same way as in the self-reenactment task.

In addition to the improvement over the body texture, we observe that avatars reconstructed with our approach often give more accurate and faithful expression control, as shown in Fig 4.
We deduce that this is because the 3DMM-driven Gaussians only need to model the head region, leading to a more accurate reconstruction of the head model and more reliable LBS weights and expression and pose blendshapes predicted by the LBS network.

### 4.5. Ablation

We show the effectiveness of the anchor constraint $\mathcal{L}_{anchor}$, test-time Euclidean transformation and warp loss $\mathcal{L}_{warp}$ in Fig 5 and 6. Even for subjects with only slight movement in the upper body, anchor constraint is still needed to learn sharp and accurate cloth texture. Besides, without anchor Gaussians and test time Euclidean transformation, the body texture is unable to align with the head Gaussians under novel poses. The warp loss $\mathcal{L}_{warp}$ is needed to prevent the neural warping field from mapping the background pixel to an arbitrary white pixel in the texture space. As anchor Gaussians only exist within the body region, the additional Euclidean transformation computed from anchor correspondences would significantly distort the background pixels, causing severe artifacts as shown in Fig 6. Additional ablation results can be found in the supplementary.

### 4.6. Rendering Efficiency

We report the number of Gaussians and the rendering speed for pure Gaussian implementation GS*, Ours, and Ours (No MLP) in Tab 4. The rendering speeds are tested on an RTX4080 Ti. For subjects wearing complicated clothes, the number of Gaussians required to model the high-frequency cloth texture significantly increases for pure Gaussian implementation, hence slowing down the rendering speed, whereas our method only models the head region with Gaussians and hence requires a much fewer number of Gaussians. The rendering speed of our no MLP version even surpasses pure Gaussian implementation for subject 005, who wears cloth with a very high-frequency texture.

Figure: Figure 3. Qualitative comparison of self-reenactment task. We show that both full version and No MLP version of our method can effectively recover a more accurate and more robust body texture, even under cases of extreme poses and high-frequency cloth textures.

Figure: Figure 4. Qualitative evaluation of cross-identity reenactment. In addition to the improvement in cloth texture quality and robustness, we found that our approach often leads to more accurate expression control. This is because we are using much fewer LBS-driven Gaussians for the body part, therefore the capacity of LBS weight inference network can fully focus on the head region.

Figure: Figure 5. Ablation. The anchor constraint is necessary for learning sharp textures even if the body only moves slightly during the video.

Figure: Figure 6. Ablation for cross-identity reenactment. The additional Euclidean transformation helps to align the body texture with head Gaussian under novel poses, whereas $\mathcal{L}_{warp}$ is necessary to prevent arbitrary warping of the white background.

## 5. Conclusion

We present Gaussian Head & Shoulders,
a method that reconstructs high-quality and animatable upper body avatars including head, chest and shoulders. By utilizing high-frequency neural texture to represent the clothed body, we are able to model sharp and robust cloth details and significantly reduce the number of Gaussians needed to represent a subject. By constraining the texture warping with a sparse set of anchor Gaussians, the body texture
is accurately mapped to the correct position even under unseen poses.
By caching the neural texture and replacing the neural warping field with a projective transformation estimated using anchor correspondences, we significantly improve rendering speed and reach over 130 FPS at novel poses, surpassing the rendering speed of pure Gaussian implementation for subjects with complicated cloth textures.

#### Limitation

Our approach cannot model avatars with extreme body rotation that causes self-occlusion in the video, as our neural texture warping does not account for self-occlusion. However, such extreme rotations are rare for common applications such as video conferencing. More discussion can be found in the supplementary.

Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping
Supplementary Material

In this supplementary material, we provide additional implementation and evaluation details in Sec A, as well as extended results including additional ablation studies, limitations, and a comparison with SMPL-driven body avatar in Sec B. Ethic discussions are in Sec C. We also highly recommend the readers to view our supplementary video.

## Appendix A Implementation Details

### A.1. Preprocessing

Our data preprocessing pipeline for extracting FLAME parameters, camera parameters and body landmarks is modified from (Zheng et al., ). After obtaining rough FLAME parameters from DECA (Feng et al., ), we further optimize the FLAME parameters to minimize the 68 facial landmarks for 3000 iterations. For subject 001, we keep the original training and test split used by PointAvatar (Zheng et al., ). For other subjects, we use the last 500 or 1000 frames as test frames, depending on the total frame count in the video.
For all subjects, we simply use the first frame as the canonical training frame for initializing anchor Gaussians and updating the anchor correspondences.
We use DWpose (Yang et al., ) to detach the noise, neck and shoulder landmarks, which are illustrated in Fig 7.

### A.2. Network Architecture

We have three MLPs in total: $\operatorname{MLP}_{d}$ which predicts the expression blendshapes $\bm{\mathcal{E}}$, pose blendshapes $\bm{\mathcal{P}}$ and LBS weights $\bm{\mathcal{W}}$ for each regular Gaussian and anchor Gaussian; $\operatorname{MLP}_{f}$ which predicts pose-dependent fine texture; $\operatorname{MLP}_{w}$ which warps view space coordinates to texture space coordinates. All three MLPs have 4 hidden layers and 128 neurons in each hidden layer. The standard Fourier frequency positional encoding (Mildenhall et al., ) is applied to the pixel coordinate, FLAME head rotation, camera translation and 2D landmarks before inputting to $\operatorname{MLP}_{f}$ and $\operatorname{MLP}_{w}$. The pixel coordinate and 2D landmarks are encoded with a frequency of 10, and camera translation and FLAME head rotation are encoded with a frequency of 2. All three MLPs are initialized to predict 0s at the beginning by setting the weights and bias of the output layer to 0. All MLPs use ReLU as the intermediate activations. Tanh is used as the final activation for $\operatorname{MLP}_{f}$, no final activation is used for $\operatorname{MLP}_{w}$, and the final activation for $\operatorname{MLP}_{d}$ are the same as (Zheng et al., ).

We use a latent dimension $D_{t}=32$ for the latent texture $\mathbf{T}_{f}$. The coarse texture $\mathbf{T}_{c}$ is initialized to be the same as the white background, while the fine latent $\mathbf{T}_{f}$ is initialized and a random and uniform distribution between $[0,1]$.

### A.3. Training Details

For all subjects, we use $\lambda_{head}=1$, $\lambda_{anchor}=1$, $\lambda_{warp}=0.025$, $\lambda_{\hat{\alpha}}=0.15$. For VGG loss weight $\lambda_{VGG}$, we set it to 0 for the first 10K iterations, and then $0.1$ for the rest of the training. This is needed as we empirically observe that training the neural texture and warping field with a strong VGG loss from the beginning severely harms their stability. The weights of FLAME regularization are initially set to $\lambda_{\bm{\mathcal{E}}}=1000,\lambda_{\bm{\mathcal{P}}}=1000,\lambda_{\bm{
\mathcal{W}}}=1$ and are reduced by half at 15k, 30k, 45k iteration respectively.

We train our model with Adam optimizer for 70k iterations in total, where the three stages of our training take 4k, 46k and 20k iterations respectively.
The learning rate for blendshapes and LBS weight MLP $\operatorname{MLP}_{d}$, neural texture, anchor Gaussian parameters and neural warping field are set to $10^{-3}$, which is halved at 30k-th and 60k-th iterations respectively. The learning rate and density control hyperparameters for regular Gaussians are the same as proposed by the original paper (Kerbl et al., ), except that we use a density gradient threshold of $2.5\times 10^{-4}$ before we start applying VGG loss, and $8\times 10^{-3}$ afterward. For every $10$k iterations during the training, we also re-project all anchor Gaussians to the image plane of the canonical image plane, and remove the anchor Guassians that are out of the view frustum. This is to prevent unconstrained anchor Gaussians from applying noisy regularization on the texture warping field.

Following (Zheng et al., ) and (Zheng et al., ), we also add a static bone, which does not take any transformation with the FLAME expression and poses.

As our preprocessing pipeline does not track eye movement, for subjects with significant eye movements in the training frames, i.e., subjects 002 and 005, we do not update the opacity and SH of regular Gaussians in the third stage to prevent undesirable view-dependent artifacts. For subjects where the semantic mask fails, i.e., subject 003, the No MLP texture may contain significant noise in the head region. We hence manually define a rough bounding box for this subject to clean the No MLP texture for self-reenactment and cross-reenactment tasks.

The training takes around 2 hours for each subject on an RTX4080 Ti.

### A.4. Evaluation Details

Following (Zheng et al., ) and (Grassal et al., ), we also fine-tune the pre-tracked FLAME expression, pose parameters, camera translation and body landmarks during the training to account for inaccuracies in the preprocessing pipeline. We use Adam optimizer with a learning rate of $10^{-4}$ and optimize them from the 30k-th iteration. For test-time tracking optimization, we only use L2 RGB loss. Since we do not have a direct gradient flowing back from the body texture to the FLAME parameters, we also optimize a translation and rotation offset for the body texture mapping.

## Appendix B Additional Results

### B.1. Videos

We strongly encourage the readers to watch the videos containing self-reenactment and cross-reenactment results in the supplementary.

As shown in the videos, existing methods either fail to model the body properly (INSTA (Zielonka et al., ), SplattingAvatar (Shao et al., )), or fail to learn the details on head and body (PointAvatar (Zheng et al., )). While the pure Gaussian Splatting baseline (GS*) could learn the face and body with much better details, it still learns blurry textures and presents severe artifacts when the subject is moving in extreme head rotation. It is most obvious for the self-reenactment and cross-reenactment videos of subject 005 – many Gaussians modeling the cloth texture are not well-aligned with each other, as a result, they cannot move naturally with the head motion. In comparison, our method can learn extremely sharp textures with robust performance under novel poses and motions.

However, we note that for subjects with extreme body motion and cloth deformation in the training, such as subject 004 and subject 007, our full version might render avatars where the body parts deform slightly unnaturally. This is because the texture-warping MLP overfits to the noise in the cloth deformation. We note that for those subjects, our No MLP version actually provides a more pleasant and natural rendering.

### B.2. Ablation

Figure: Figure 7. Landmarks. We use DWPose (Yang et al., ) to detect nose, neck and shoulder landmarks to use as input to $\operatorname{MLP}_{f}$ and $\operatorname{MLP}_{w}$.

Table: Table 3. Quatitative ablation. We show the anchor constraint is necessary for learning sharp and correct body texture. While the warp loss might not necessarily improve the performance for the self-reenactment task, it is needed for cross-reenactment with out-of-distribution poses.

### B.3. Texture Cleaning

Figure: Figure 9. Qualitative comparison with full body avatar methods. Due to the limited landmarks available on the shoulders and chest, existing SMPL tracking methods fail to obtain correct SMPL parameters. Fully body neural avatars that rely on SMPL hence fail to learn accurate and robust body. While our method does not include SMPL 3DMM, the use of static virtual bone and neural texture warping allow us to learn the body texture accurately.

Figure: Figure 10. Texture cleaning. We show the body texture without masking (a) and with cleaning (b), as well as the rendering without texture cleaning (c) and with texture cleaning (d).

When distilling the pose-dependent fine texture into the coarse texture for our no MLP version, we utilized DeepLabV3 (Chen et al., ) to obtain a coarse mask of the background and set the values of those pixels to 1. This is needed because the body texture contains a padding region to account for the body part that is moving in and out during the video. A majority section of the padding, especially the padding region on the top the left and right sides, are rarely used and trained during optimization. As a result, the fine texture colors obtained in those regions can produce noisy artifacts; see Fig 10.

### B.4. Comparison with Full Body Avatars

Table: Table 4. Body Only Quantitative Comparison with Full Body Avatars. We show that existing full body neural avatar methods that rely on SMPL deformation perform significantly worse than our methods. Metrics are computed after masking out the background and head regions.

### B.5. Limitations

Although we propose a no MLP version that is able to render at novel poses with 130 FPS, as it completely relies on rigid homography transformation to map body texture to the view space, it is unable to model any non-rigid deformation in the body. In addition, for sequences with extreme head rotations, it might move the body in a way that is not exactly aligned with the ground truth, as shown in the supplementary videos.
However, we observe that the results produced with this no MLP version still present a faithful rendering.
For cases where the non-rigid body deformation is important, we recommend the use of the full version, whose rendering speed is around 70 FPS and can be further optimized by caching the fine texture only.

## Appendix C Ethics

We captured 4 human subjects with mobile phones for our experiments. All subjects have signed consent forms for using the captured video in this research and publication. We will release the data for subjects with permission.

Our method constructs faithful and animatable head avatars and can be used to generate videos of real people performing synthetic poses and expressions. We do not condone any misuse of our work to generate fake content of any person with the intent of spreading misinformation or tarnishing their reputation.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Considering the 3DMM parameters and landmarks of the shoulder are acquired separately, is there a method to unify the two tracking results? It seems that the head and shoulder are misaligned in some frames.
soundness: 3
strengths: The paper has a clear structure, and it‘s easy to follow.
The idea of modeling the head and shoulder separately is interesting. The neural texture strategy can model the shoulders efficiently with less 3D Gaussians.
The pipeline is verified on different human subjects.
summary: This paper proposes a method to reconstruct upper body avatars from videos based on 3D Gaussian Splatting. The method models head and shoulders separately, i.e., heads by 3D Gaussians anchored on 3DMM template, and shoulders by Anchor Gaussians and neural textures. The paper proposes a Neural Texture strategy to model shoulders with less 3D Gaussians. The authors evaluate their methods on different experimental datasets and human subjects.
weaknesses: The method models head and shoulders separately, i.e., different Gaussian representations and different tracking approaches. This causes a limitation, i.e., the head and shoulders are misaligned for some relatively big body movements. 
 
It seems that the Anchor Gaussian only supports relatively simple should motions. It’s not verified that the proposed method can model pose-dependent wrinkles for the shoulders in the paper. The body movements in the supplementary demo are too simple, and I cannot observe that the clothing wrinkles vary with poses.

---

confidence: 3
contribution: 2
initial_rating: 5
presentation: 1
questions: 1) As mentioned above, I’m curious to see novel out-of-plane renders of the shoulders themselves to see how geometrically accurate they are. 

2) There is no ablation of exactly how robust the proposed method is to shoulder movement in the capture video. Understanding this would help evaluate the method more throughly (I do grant that prior work does not even address this problem, but I believe this is important to make the paper more complete.)

3) Any reason why Splatting-avatar results are so much worse compared to the other methods?
soundness: 3
strengths: 1) The paper is well written and easy to follow
2) The use of a neural texture map to model the shoulders is well motivated and intuitive
3) The additional loss terms introduced are well ablated in both the main paper and the supplementary
4) The proposed method both qualitatively and quantitatively outperforms prior work
summary: This paper proposes a Gaussian splatting based deformable avatar method with a specific focus on modelling shoulders with a high fidelity. The head is represented by a set of 3D Gaussians while the shoulders are represented by a neural texture map. The gaussians on the head are deformed from the canonical space using Linear Blend Skinning while the texture-map for the shoulders are warped using a pose dependent MLP. Once in the deformed space, both the neural textures and the gaussians are rendered to yield the final image. The properties of the gaussians and the warpings are learnt through a set of RGB and deformation based losses. The neural texture warping is specifically constrained through an anchor loss defined using a set of Gaussians learnt on the shoulders. Both quantitative and qualitative show improvements over prior work.
weaknesses: 1) There are no significant out-of-plane view synthesis results that are shown in the results to demonstrate whether or not the learn texture map is able to generate novel renders of the shoulders in a geometrically consistent way or it just renders them as a plane 

2) This paper is, I believe, a little out of scope of ICLR and is more suited at a vision or graphics conference. However, I will defer to other reviewers and the AC for that before I consider it as a strong weakness.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: - Since the modeling of the body parts starts from a canonical frame/space, will the selection of this canonical frame significantly affect the reconstruction quality?
- I still have doubts about how this method drives the reconstruction of the upper body of the character for unseen self-driving frames and cross-driving frames. From the visual results, it seems that the upper body is not aligned with the driving frame.
soundness: 2
strengths: - This paper uses neural textures to model body parts simultaneously, instead of just the head.
- The paper proposes a method to accelerate reasoning, so that the rendering speed can reach 130 frames per second.
summary: This paper introduces a method to create a high-fidelity head avatar.
This method maps intricate texture to the image plane through a sparse set of anchor Gaussians driven by 3DMM, not only reconstructs the head, but also the upper body parts.
In addition, this paper also proposes a method to use Gaussian spraying to speed up rendering.
weaknesses: - This paper lacks comparisons with some methods in terms of head reconstruction quality, including [1, 2].
- The head part of this method is similar to the theory of existing methods (PSAvatar).
- The tracking results given in Figure 11 are not convincing. In many body tracking projects, some scenes containing half of the body can still produce reasonable results. For example, One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer. The paper should describe in more detail the methods used for tracking and why they were failed, otherwise I don't see the point why not using the upper body SMPL model.

- Reference

        [1] Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians
        [2] GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. FlashAvatar is motivated by inference acceleration. Has this paper calculated its FPS?
soundness: 4
strengths: 1. This method effectively models detailed upper body textures.
2. This paper combines 3DGS and neural fields to construct head and body textures, showcasing technical novelty.
3. Experimental results demonstrate that this method has significant advantages in rendering accuracy and inference speed while using a smaller number of 3D Gaussians.
summary: The paper addresses the challenge of creating realistic upper body avatars from monocular videos. Existing methods focus on head avatars and struggle with detailed body textures, resulting in blurry reconstructions. The proposed solution uses neural textures guided by sparse Gaussians to improve fidelity and speed, achieving up to 130 FPS, and excels in both self and cross-reenactment tasks.
Contributions:
1. It uses anchor Gaussians to effectively model detailed upper body textures.
2. It eliminates MLP queries for faster speeds, reaching about 130 FPS.
weaknesses: 1. The article lacks some clarity in its writing. For example, it does not explain what texture space is in the introduction.
2. The article could include additional baselines, such as Gaussian Avatars and Gaussian Head Avatar.
3. Regarding the rendering formula in Section 3.5, the coefficient for the third term, body texture, is the product of the opacities of the first two terms. Wouldn't this cause most of the pixels' colors in the third term to be overridden?

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: 1. Can the authors provide the novel view synthesis for rendering the shoulder and also present the volumetric representation of the shoulder areas to let readers know how robust the model is for shoulder rendering?

2. What exactly each module brings or benefits for the shoulder rendering? I think the following are possible ablations: 
(a) completely remove anchor gaussian and the 3D-2D correspondence, only relying on neural texture like in StyleAvatar
(b) exclude Neural texture, use anchor gaussian only

3. For the anchor gaussian, how the deformation achieved? If it is relying on LBS, due to that LBS is based on flame mesh that only contains the head and neck, there does not exist a real LBS for the shoulders. How should we understand the relationship between the deformation of shoulder gaussian point and neural texture? Is it a refinement of potential noisy deformed gaussian points?
soundness: 2
strengths: The overall writing is easy to following and well-written.

It seems like compared with conventional method, which ignore the shoulder but only focus on face, this model presents better performance and more robust to shoulder renderings (like compare with PointAvatar and GS*)
summary: This work present the way of rendering shoulders in addition to the human faces. In general, the author propose the following methods:
(1) to use anchor gaussians to represent the shoulder region only.
(2) use neural texture to refine the shoulder represent and make it robust to body motions including translation, rotation, and depth-based motions;
(3) Leverage 3D-to-2D correspondence mapping as the regularization of the shoulder learning.
(4) an option without using MLP for fast rendering

The author conducts many experiments to validate their method.
weaknesses: Concerns of Comparisons:

1. Some works already tries to explore the joint face and shoulder rendering including:
   a. Real3D-Portrait, which uses FOMM for the shoulder motion reenactment and finally merge the face motion and shoulder motion together. 
  b. StyleAvatar, a 2D human face rendering technique which includes the face, shoulder and background together

The author did not include these two into the comparison. Is it possible to compare with this pipepile with FOMM/TPS-based methods used in Read3D-Portrait / Torso NeRFs used previously in GeneFace to see whether this method is more effective? In addition, using neural texture as an whole image enhancement method is also covered in StyleAvatar, but slightly different: this work tries to enhance the shoulder only while StyleAvatar's neural texture is for the whole image. It will be good if the authors can present very strong argument regarding this given my evaluation might be partially wrong.

Data:
The videos in the supplementary material do not present very large shoulder motions. I am not quite sure whether the model is robust in rendering the shoulder. However, I do see for slight shoulder motions, the model's performance is good.

Experiment:
I did not see the ablations for the main method part. The proposed methods include introducing the anchor gaussians for shoulder only, the 3d-2d correspondence, the neural texture compared with the baseline GS and PointAvatar. I will defer this part in the questions.

Meta-review: This paper proposes a method for constructing a head and shoulders avatar from a provided video of a subject. The main innovation proposed in the approach is to employ regular Gaussian splatting for the face along with sparse anchor Gaussians and a view-dependent neural texture for the relatively simpler shaped shoulder region, which normally has more complex texture from clothing. The latter approach is motivated by the fact that highly textured regions require many Gaussians to model the texture making the approach unnecessarily computationally expense, which can be saved by employing warping of neural textures instead. The authors show state of the art results both in terms of accuracy and speed versus the existing state of the art.

The work address a relatively un-addressed problem in 3D human avatar creation of including the shoulders as well. The majority of the existing works constrain themselves to modeling the face region only. In most practical applications e.g., video conferencing, however, the lack of shoulders, severely limits realism and hence the end user experience.

On the flip side, the major limitation of this technique is its dependence on two separate approaches for tracking the face and shoulder movements, which leads to inconsistent seams between them. This can be partially addressed by using SMPL-X-based full body tracking methods like OSX, but the proposed method still is limited in only being able to accurately model small shoulder motions like rotations and translations and not larger ones like shrugging. This limits the novelty of the method in mainly proposing a novel way to efficiently handle the modeling highly textured regions of the human body only.

Decision: Accept (Poster)