Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos

Ziqian BaiFeitong TanZeng HuangKripasindhu SarkarDanhang TangDi QiuAbhimitra MekaRuofei DuMingsong DouSergio Orts-Escolano

article2023CVPR65 citations

Presents a hybrid pipeline that reconstructs photorealistic, user-controllable 3D head avatars from casual monocular RGB videos by anchoring CNN-predicted local UV features onto a parametric face mesh to guide a neural radiance field.

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Generating realistic and controllable 3D human head avatars is essential for emerging digital communication, virtual reality, gaming, and visual effects. However, existing high-fidelity systems typically rely on expensive multi-camera rigs or specialized depth sensors, creating high barriers to broader adoption. Previous attempts to build controllable avatars from standard, casual smartphone or webcam video struggle with a fundamental trade-off: traditional surface models fail to capture dynamic features like hair, wrinkles, and accessories, while newer neural rendering methods often produce over-smoothed results, blurry artifacts, or distorted facial geometry during novel expressions.

The article demonstrates a novel framework that learns a personalized, photorealistic, and fully controllable 3D volumetric head avatar using only a single, short monocular video clip of one to two minutes. The primary objective is to achieve fine-grained control over head poses and facial expressions while preserving subject-specific details across novel camera viewpoints.

To achieve this, the approach anchors a neural radiance field to the geometry of a 3D Morphable Model (a standard parametric face mesh). Instead of relying on global networks that struggle to capture fine details, the method predicts spatially local, expression-dependent features anchored directly to the face mesh vertices. These dynamic features are generated by rasterizing vertex displacements into a 2D surface map and processing them with a convolutional neural network. Volumetric color and density at any point in 3D space are then decoded locally by interpolating adjacent vertex features, with per-frame error correction used during training to account for tracking inaccuracies.

The evaluation yields several key findings across quantitative metrics and visual assessments. First, the proposed displacement-driven model consistently outperforms current state-of-the-art monocular avatar methods across standard perceptual and image quality benchmarks (LPIPS, SSIM, and PSNR). Second, using convolutional networks over surface vertex displacements provides critical spatial context, significantly outperforming standard multilayer perceptrons in synthesizing fine details like teeth, cheek contours, and reflections on eyeglasses. Third, the system demonstrates superior generalization and robustness when rendering unseen expressions or extrapolated, extreme facial movements. Finally, when evaluated on training data restricted to only 50% of the video clip, the method degrades far less than alternative designs, confirming that high-quality avatars can be generated from very brief user captures.

These findings prove that high-end 3D avatars can be democratized using standard consumer hardware without requiring specialized studio environments, significantly lowering production costs and deployment friction for digital human applications. However, organizations evaluating this technology should account for key operational trade-offs and limitations. Training remains subject-specific and rendering relies on volumetric neural fields, which are computationally intensive and not yet optimized for low-latency, real-time edge environments. Additionally, because the architecture is anchored to a facial parametric model, it cannot synthesize structures absent from the underlying mesh, such as the tongue or the torso.

Next steps for development include exploring model compression and acceleration to enable real-time mobile rendering, extending the geometric anchor to support upper-body and full-body avatars, and implementing proactive governance measures. Given the realism of the synthesized heads, deploying teams should integrate cryptographic signatures or digital watermarking to prevent potential deepfake misuse and ensure content authenticity.

Google Simon Fraser University avatar virtual agent monocular image volumetric head avatars monocular RGB videos volumetric rendering

arXiv: 2304.01436

Paper: RigNeRF: Fully Controllable Neural 3D Portraits, ShahRukh Athar et al. (2022). RigNeRF introduces deformation fields guided by 3D morphable models to control neural radiance fields from monocular portrait video, establishing the core hybrid methodology that the source paper builds upon.
Paper: Learning a model of facial shape and expression from 4D scans, Tianye Li et al. (2017). FLAME provides the articulated 3D morphable head model that establishes the geometric prior and parametric facial control utilized by the source paper.
Paper: Nerfies: Deformable Neural Radiance Fields, Keunhong Park et al. (2020). Nerfies establishes foundational techniques for optimizing continuous deformation fields to reconstruct dynamic and non-rigid human subjects from monocular video.
Paper: D-NeRF: neural radiance fields for dynamic scenes, Albert Pumarola et al. (2021). D-NeRF introduces the canonical space and time-conditioned deformation field framework for dynamic neural radiance fields that underpins volumetric avatar reconstruction.
Paper: A Morphable Model For The Synthesis Of 3D Faces, Volker Blanz et al. (1999). This seminal paper introduces 3D Morphable Models (3DMM), defining the parametric representation of facial geometry and texture that the source relies on for expression control.
Paper: FENeRF: Face Editing in Neural Radiance Fields, Jingxiang Sun et al. (2022). FENeRF establishes methods for decoupling facial geometry and semantics in 3D-aware neural radiance fields to enable local editing.

Paper: GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians, Shenhan Qian et al. (2024). GaussianAvatars extends mesh-anchored animatable head avatar modeling by attaching explicit 3D Gaussian splats to parametric face geometry for real-time, high-fidelity rendering.
Paper: Relightable Gaussian Codec Avatars, Shunsuke Saito et al. (2024). Relightable Gaussian Codec Avatars advances animatable head representations by integrating relightable appearance modeling and fine-scale geometric detail into Gaussian-based avatar frameworks.
Paper: Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis, Zhenhui Ye et al. (2024). Real3D-Portrait advances 3D talking avatar synthesis by generalizing high-fidelity 3D facial animation to one-shot reference images driven by audio or video.
Paper: FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models, Shivangi Aneja et al. (2024). FaceTalk builds upon expressive neural volumetric head representations by learning audio-driven latent diffusion models for dynamic facial animation.
Paper: 3D Geometry-aware Deformable Gaussian Splatting for Dynamic View Synthesis, Zhicheng Lu et al. (2024). This paper advances deformable dynamic view synthesis by pairing canonical 3D Gaussian representations with geometry-aware deformation networks.

Cover for Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos

Abstract

We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses. Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a neural radiance field to achieve fine-grained control and photorealism. To reduce over-smoothing and improve out-of-model expressions synthesis, we propose to predict local features anchored on the 3DMM geometry. These learnt features are driven by 3DMM deformation and interpolated in 3D space to yield the volumetric radiance at a designated query point. We further show that using a Convolutional Neural Network in the UV space is critical in incorporating spatial context and producing representative local features. Extensive experiments show that we are able to reconstruct high-quality avatars, with more accurate expression-dependent details, good generalization to out-of-training expressions, and quantitatively superior renderings compared to other state-of-the-art approaches.

Topics

avatar virtual agent monocular image

Keywords

volumetric head avatars monocular RGB videos volumetric rendering volumetric performance capture view synthesis neural textures facial landmarks facial geometry image animation

1. Introduction
2. Related Works
3. Method
3.1. Avatar Representation
3.2. Predicting Expression-Dependent Features
3.3. Training Schema
4. Experiments
4.1. Datasets and Metrics
4.2. Comparisons with State-of-Art
4.3. Driving the Avatar
4.4. Ablation Study: Expression Features
4.5. Robustness to Expression Extrapolation
5. Discussion
References

Knowls

Knowl 1 — 3DMM-Anchored Neural Radiance Field Architecture
model/method
A 3D head avatar is represented by anchoring a dynamic neural radiance field to the deformable mesh surface of a 3D Morphable Model (such as FLAME). Let $V_i = V(eta, oldsymbol{ heta}_i, oldsymbol{oldsymbol{ heta}}_i) \subset \mathbb{R}^3$ denote the 3DMM mesh vertices deformed by identity shape $\beta$ , expression parameters $\boldsymbol{\psi}_i$ , and pose parameters $\boldsymbol{\theta}_i$ (including neck, jaw, and eye rotations) at frame $i$ . Each mesh vertex $v_i^j$ is assigned a dynamic feature vector $z_i^j$ .

To compute the volume density $\sigma_i(q)$ and RGB color $c_i(q, d_i)$ at an arbitrary 3D query point $q \in \mathbb{R}^3$ for ray direction $d_i \in \mathbb{S}^2$ , the $k$ -nearest neighbor ( $k$ -NN) vertices $\mathcal{N}_k^q$ of $q$ on the mesh $V_i$ are identified. The local features are transformed and aggregated using two Multi-Layer Perceptrons (MLPs), $F_0$ and $F_1$ , weighted by inverse Euclidean distance:

$\hat{z}_i^j = F_0(v_i^j - q, z_i^j)$

$\hat{z}_i = \sum_{j \in \mathcal{N}_k^q} w^j \hat{z}_i^j, \quad \text{where } w^j = \frac{d^j}{\sum_{m \in \mathcal{N}_k^q} d^m}, \quad d^j = \frac{1}{\|v_i^j - q\|_2^2}$

$c_i(q, d_i), \sigma_i(q) = F_1(\hat{z}_i, d_i)$

Given a camera ray $r(t) = o + t d_i$ with near bound $t_n$ and far bound $t_f$ , the rendered pixel color $C_i(r)$ is obtained through numerical volume rendering:

$C_i(r) = \int_{t_n}^{t_f} T(t) \sigma_i(r(t)) c_i(r(t), d_i) \, dt, \quad \text{where } T(t) = \exp\left(-\int_{t_n}^t \sigma_i(r(s)) \, ds\right)$
Knowl 2 — UV-Space Expression-Dependent Feature Learning
model/method
To capture fine-grained, dynamic expression variations and overcome the capacity limitations of global MLPs, dynamic vertex features $z_i^j$ are synthesized via a 2D Convolutional Neural Network operating in the 3DMM texture atlas space (UV space).
1. Vertex Displacement Formulation (Ours-D): Per-vertex 3D displacement vectors are computed relative to the neutral mesh configuration:
$D_i = V_i(\boldsymbol{\psi}_i, \boldsymbol{\theta}_i) - V_{\text{neutral}}(0, 0)$

where $V_i(\boldsymbol{\psi}_i, \boldsymbol{\theta}_i)$ is the posed/expressed mesh and $V_{\text{neutral}}(0, 0)$ is the neutral template mesh. The displacement vectors $D_i$ are rasterized into a 2D UV-space map and processed through a 2D convolutional U-Net $\mathcal{F}_D$ . The resulting 2D feature map is sampled back onto the mesh vertices $V_i$ using UV coordinates to yield the dynamic feature vectors $\{z_i^j\}$ .
1. 1D Parameter Decoding Variant (Ours-C): In an alternative formulation, a transposed convolutional decoder processes the concatenated 1D parameter vectors $[\boldsymbol{\psi}_i, \boldsymbol{\theta}_i]$ directly into the 2D UV feature map.
Conditioning the network on explicit per-vertex displacement fields in UV space (Ours-D) provides spatial convolutional context between adjacent facial regions, improving robustness to extreme or out-of-training expressions.
Knowl 3 — Error-Correction Warping Field for Monocular Avatars
model/method
To compensate for tracking noise, non-rigid hair motion, and registration misalignments between the parametric 3DMM mesh and the ground-truth video frames, an error-correction warping field is optimized during training.

For frame $i$ , an MLP $\mathcal{F}_E$ takes the spatial query point $q \in \mathbb{R}^3$ and an optimizable per-frame latent code $e_i$ to output a spatial transformation $T_i(q)$ :

$q' = T_i(q) = \mathcal{F}_E(q, e_i)$

The warped query point $q'$ is used in place of $q$ to find the $k$ -NN mesh vertices and evaluate color and density. This warping module is active exclusively during training to absorb unmodeled per-frame tracking discrepancies and is disabled at inference time to ensure strict 3DMM-driven controllability.
Knowl 4 — Optimization Objective and Regularization Schema
equation
The complete avatar model parameters—including the UV feature extraction network, the local MLPs $F_0$ and $F_1$ , and the deformation field $\mathcal{F}_E$ with latent codes $e_i$ —are trained end-to-end on monocular RGB frames using the total loss function:

$\mathcal{L} = \mathcal{L}_{rgb} + \lambda_{\text{elastic}} \mathcal{L}_{\text{elastic}} + \lambda_{\text{mag}} \mathcal{L}_{\text{mag}}$

where:

$\mathcal{L}_{rgb} = \sum_{i} \sum_{r} \|C_i(r) - I_i(r)\|_2^2$

penalizes the $\ell_2$ photometric difference between rendered ray colors $C_i(r)$ and ground-truth pixel values $I_i(r)$ ;

$\mathcal{L}_{\text{mag}} = \sum_{q} \|q - T(q)\|_2^2$

constrains the magnitude of the predicted error-correction warping field $T(q)$ ; and $\mathcal{L}_{\text{elastic}}$ penalizes non-rigid deformations in the warping field.

The hyperparameter weights are set to $\lambda_{\text{mag}} = 10^{-2}$ , while $\lambda_{\text{elastic}}$ is initialized to $10^{-4}$ and decayed to $10^{-5}$ after 155,000 optimization iterations.

Knowl 5 — Quantitative Avatar Reconstruction and Novel View Benchmark Comparison

data/table

Models are evaluated on monocular RGB video test splits using Learned Perceptual Image Patch Similarity (LPIPS, lower is better), Structural Similarity Index Measure (SSIM, higher is better), and Peak Signal-to-Noise Ratio (PSNR in dB, higher is better). The comparison includes 2D motion transfer methods (TPSMM, FOMM) and 3D avatar approaches (NHA, IMAvatar, NerFACE), evaluated across five subjects.

Method	Subject 0	Subject 1	Subject 2	Subject 3	Subject 4
	LPIPS / SSIM / PSNR	LPIPS / SSIM / PSNR	LPIPS / SSIM / PSNR	LPIPS / SSIM / PSNR	LPIPS / SSIM / PSNR
TPSMM	0.192 / 0.852 / 22.60	0.205 / 0.830 / 16.38	0.216 / 0.782 / 18.40	0.222 / 0.799 / 20.28	0.156 / 0.913 / 21.29
FOMM	0.171 / 0.841 / 22.93	0.179 / 0.827 / 16.02	0.202 / 0.777 / 18.98	0.186 / 0.798 / 22.28	0.122 / 0.915 / 23.94
NHA	0.165 / 0.836 / 20.20	0.166 / 0.840 / 15.48	0.178 / 0.809 / 17.99	0.153 / 0.798 / 21.31	0.091 / 0.926 / 23.78
IMAvatar	0.207 / 0.852 / 21.26	0.187 / 0.848 / 15.98	0.265 / 0.729 / 15.80	0.214 / 0.782 / 20.37	0.142 / 0.897 / 20.63
NerFACE	0.205 / 0.817 / 20.06	0.182 / 0.833 / 15.78	0.188 / 0.793 / 19.41	0.229 / 0.747 / 18.16	0.093 / 0.938 / 25.57
Ours-D	0.144 / 0.864 / 21.92	0.152 / 0.855 / 16.23	0.141 / 0.841 / 20.42	0.156 / 0.833 / 23.05	0.075 / 0.944 / 25.71

Ours-D consistently achieves the lowest perceptual distortion (LPIPS) across all subjects, outperforming both global MLP neural radiance field methods (NerFACE, IMAvatar) and neural mesh texture methods (NHA).

Knowl 6 — Ablation Study on Expression Feature Encoding and Data Sparsity

data/table

Ablations evaluate different feature conditioning mechanisms on LPIPS perceptual error (lower is better) across four subjects, comparing full training sequences against reduced training sets consisting only of the first 50% of frames.

Configuration	Subject 0	Subject 1	Subject 2	Subject 3
Full Training Data
Static Features	0.1559	0.1586	0.1552	0.1688
3DMM Codes	0.1599	0.1620	0.1746	0.1738
3DMM Codes MLP	0.1568	0.1551	0.1505	0.1686
Ours-C	0.1417	0.1457	0.1383	0.1550
Ours-D	0.1439	0.1523	0.1415	0.1559
First 50% Training Data
Ours-C	0.2038 (+0.0621)	0.1483 (+0.0026)	0.1580 (+0.0197)	0.1566 (+0.0016)
Ours-D	0.1711 (+0.0272)	0.1511 (-0.0012)	0.1516 (+0.0101)	0.1558 (-0.0001)

Key observations:

UV-CNN architectures (Ours-C, Ours-D) outperform global/per-vertex MLP decoders (Static Features, 3DMM Codes, 3DMM Codes MLP) because CNN operations capture spatial context between neighboring vertices.
When training data is halved, Ours-D (UV displacement input) generalizes to unseen expressions with significantly smaller degradation ( $+0.0272$ LPIPS on Subject 0) compared to Ours-C ( $+0.0621$ LPIPS).

Knowl 7 — Limitations of 3DMM-Anchored Neural Head Avatars
limitation
The 3DMM-anchored neural head avatar approach has the following limitations:
1. 3DMM Representation Bounds: The avatar is strictly driven by the expression parameter space of the underlying linear 3DMM (FLAME). Structures that are not represented in the parametric model (such as the tongue and inner mouth geometry) cannot be synthesized or controlled.
2. NeRF Computational Overhead: The pipeline inherits volumetric neural radiance field training and rendering overhead, resulting in time-consuming subject-specific optimization and non-real-time volumetric raymarching rendering.
3. Domain Restriction: The model reconstructs only portrait head avatars and does not extend to upper-body, clothing, or full-body motion capture.

Coverage note — None was omitted; all key contributions—including geometry-anchored NeRF formulation, UV displacement feature learning, error-correction deformation, loss functions, quantitative benchmarks, ablation studies, and limitations—are fully covered.

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Citation

MLA

Bai, Z., et al. “Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos”. arXiv, 2023, http://arxiv.org/abs/2304.01436v1.

APA

Bai, Z., Tan, F., Huang, Z., Sarkar, K., Tang, D., Qiu, D., Meka, A., Du, R., Dou, M., Orts-Escolano, S., Pandey, R., Tan, P., Beeler, T., Fanello, S., & Zhang, Y. (2023). Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos. arXiv. http://arxiv.org/abs/2304.01436v1

Chicago

Bai, Z., F. Tan, Z. Huang, et al. 2023. “Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos”. arXiv. http://arxiv.org/abs/2304.01436v1.

Harvard

Bai, Z. et al. (2023) “Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos”, arXiv [Preprint]. Available at: http://arxiv.org/abs/2304.01436v1.

Vancouver

1. Bai Z, Tan F, Huang Z, et al (2023) Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos. arXiv

BibTeX

@article{bai2023learning,
  title = {Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos},
  author = {Bai, Ziqian and Tan, Feitong and Huang, Zeng and Sarkar, Kripasindhu and Tang, Danhang and Qiu, Di and Meka, Abhimitra and Du, Ruofei and Dou, Mingsong and Orts-Escolano, Sergio and Pandey, Rohit and Tan, Ping and Beeler, Thabo and Fanello, Sean and Zhang, Yinda},
  year = {2023},
  journal = {arXiv},
  url = {http://arxiv.org/abs/2304.01436v1},
  eprint = {2304.01436}
}

Metadata:arXiv

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Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos

Abstract

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Table of Contents

Knowls

Knowl 1 — 3DMM-Anchored Neural Radiance Field Architecture

Knowl 2 — UV-Space Expression-Dependent Feature Learning

Knowl 3 — Error-Correction Warping Field for Monocular Avatars

Knowl 4 — Optimization Objective and Regularization Schema