We present MATCH (Multi-view Avatars from Topologically Corresponding Heads), a multi-view Gaussian registration method for high-quality head avatar creation and editing. State-of-the-art multi-view head avatar methods require time-consuming head tracking followed by expensive avatar optimization, often resulting in a total creation time of more than one day. MATCH, in contrast, directly predicts Gaussian splat textures in correspondence from calibrated multi-view images in just 0.5 seconds per frame, without requiring data preprocessing. The learned intra-subject correspondence across frames enables fast creation of personalized head avatars, while correspondence across subjects supports applications such as expression transfer, optimization-free tracking, semantic editing, and identity interpolation. We establish these correspondences end-to-end using a transformer-based model that predicts Gaussian splat textures in the fixed UV layout of a template mesh. To achieve this, we introduce a novel registration-guided attention block, where each UV-map token attends exclusively to image tokens depicting its corresponding mesh region. This design improves efficiency and performance compared to dense cross-view attention. MATCH outperforms existing methods in novel-view synthesis, geometry registration, and head avatar generation, while making avatar creation 10 times faster than the closest competing baseline. The code and model weights are available on the project website.
Figures & tables
Figure 2: Overview . Given calibrated multi-view input images, MATCH first predicts a coarse mesh registration using a pretrained network. We obtain RGB and XYZ textures combined with learnable positional embeddings to encode UV tokens and follow GS-LRM [ 67 ] to tokenize the input images. The image and UV tokens serve as input to a transformer with two alternating attention blocks. In the novel registration-guided attention block, we render UV coordinate images from the input views, and for each UV token restrict the attention to image tokens displaying the relevant mesh region. The subsequent grouped attention block performs attention across the UV tokens and the tokens of each input image separately. The transformer outputs processed UV tokens that are projected into a texture of Gaussians.
Figure 3: Correspondence score estimation between image tokens and UV tokens. To ease visualization, the full mesh is rasterized in overlay with the UV renders and patch sizes are increased.
Figure 4: Novel view synthesis comparison on Ava-256 [ 45 ] . MATCH exhibits superior synthesis quality.
Dataset
Method
LPIPS ↓
CSIM ↑
PSNR ↑
SSIM ↑
L1 ↓
L2 ↓
Ava-256 [ 45 ]
GPAvatar [ 7 ]
0.303
0.507
21.304
0.714
0.046
0.009
FastAvatar [ 63 ]
0.285
0.667
15.880
0.730
0.074
0.032
LAM [ 21 ]
0.273
0.678
15.898
0.734
0.078
0.035
Avat3r [ 29 ]
0.274
0.626
22.722
0.745
0.039
0.007
FaceLift [ 44 ]
0.208
0.868
21.661
0.825
0.038
0.010
Ours
0.163
0.928
23.680
0.848
0.027
0.008
Table 1: Novel view synthesis results on Ava-256 and NeRSemble.
Figure 5: Novel view synthesis on NeRSemble. Ours (Ava) / Ours (NeRSemble) are trained on Ava-256 and NeRSemble only, respectively.
Figure 6: Ablation experiment on Ava-256.
LPIPS ↓
CSIM ↑
PSNR ↑
SSIM ↑
L1 ↓
L2 ↓
Dense Attention
0.221
0.849
20.364
0.794
0.041
0.013
w/o Sapiens
0.202
0.907
22.104
0.819
0.034
0.010
w/o Skipconn.
0.192
0.913
23.075
0.816
0.032
0.009
Orig. TEMPEH
0.190
0.909
22.775
0.823
0.033
0.009
Huv=Wuv=256
0.194
0.907
22.830
0.817
0.033
0.009
Huv=Wuv=512
0.190
0.914
22.829
0.822
0.032
0.009
Table 2: Ablation experiments on Ava-256.
Figure 7: Interpolation between Gaussian textures by factor γ .
Figure 8: (Top) Semantic editing: Replacing a source’s nose with that of a target. (Bottom) Expression transfer: Jaw articulation and mouth interior are transferred from a target to a source identity.
Figure 9: Geometry reconstruction comparison. The heatmaps visualize the Euclidean distance between the predicted and ground truth vertices. ’TEMPEH Glob. ∗ ’ is the adapted version of TEMPEH’s global stage that we use for the coarse mesh registration.
Full
Face
Mouth
Eyes
P2P ↓
P2S ↓
P2P ↓
P2S ↓
P2P ↓
P2S ↓
P2P ↓
P2S ↓
TEMPEH Glob. ∗
7.34
2.76
3.84
1.50
2.69
1.15
2.27
1.09
TEMPEH [ 5 ]
7.84
2.18
4.04
0.97
2.99
0.69
2.66
0.62
Ours
6.69
2.10
3.18
0.88
2.14
0.72
1.54
0.61
Table 3: Quantitative geometry reconstruction comparison on Ava-256. Scores are reported in mm. P2P: Point-to-Point distance. P2S: Point-to-Surface distance. ’TEMPEH Glob. ∗ ’ is the adapted version of TEMPEH’s global stage that we use for the coarse mesh registration. ’Face’ is the facial area w/o mouth, eyes, and ears.
Figure 10: Qualitative comparison for image-based self-reenactment (top) and cross-reenactment (bottom) of the subject-specific avatars.
Reconstr.
Self-Reenactment
Cross-Reenactment
Time ↓
LPIPS ↓
SSIM ↑
PSNR ↑
CSIM ↑
EmoL1 ↓
GA [ 52 ]
15.5h
0.233
0.755
21.248
0.629
10.618
RGBAvatar [ 32 ]
11.4h
0.233
0.782
22.185
0.657
10.333
GEM [ 70 ]
45.3h
0.214
0.778
21.761
0.800
9.866
Ours
4.6h
0.174
0.809
24.122
0.813
9.837
Table 4: Quantitative comparison of subject-specific head avatars.
Figure 11: Limitations. a) Direct expression transfer ( Section 4.3 ) may incur identity leakage for dissimilar identities. b) The meshes extracted from the predicted Gaussian 3D location textures ( Section 4.4 ) can exhibit self-intersecting surfaces. c) The subject-specific head avatars ( Section 3.2 ) are bound to interpolations of the training expressions and do not track eye movement.
Table 5: MATCH loss weights in different training stages.
Parameter
Value
UV texture resolution Huv×Wuv
1024×1024
UV token patch size puv
16
Image token patch size pimg
8
Token dimension d
512
Number of input images V
12
Image resolution Himg×Wimg
640×512
Appendix
Table 6: MATCH hyperparameters.
Figure 13: Novel view synthesis comparison against CAP4D on Ava-256.
Figure 14: Additional semantic editing results. From top to bottom: Transferring beard and lips, eyes, and hairstyle.
VTEMPEH
VMATCH
LPIPS ↓
CSIM ↑
PSNR ↑
SSIM ↑
L1 ↓
L2 ↓
12
2
0.213
0.866
20.503
0.795
0.042
0.013
12
4
0.212
0.865
21.078
0.798
0.039
0.012
12
8
0.195
0.907
22.350
0.816
0.034
0.010
12
12
0.187
0.918
23.032
0.825
0.032
0.009
12
16
0.182
0.923
23.367
0.831
0.032
0.009
2
2
0.261
0.689
15.996
0.731
0.071
0.033
Appendix
Table 7: Quantitative ablation of the number of input views to MATCH evaluated on Ava-256. We evaluate two scenarios: i) Changing the number of input views to MATCH while keeping the number of inputs to the coarse mesh registration model (TEMPEH) at the default ( V=12 ). ii) Changing the number of input views for both TEMPEH and MATCH.
Figure 15: Qualitative ablation study for the number of input views to MATCH. We evaluate two scenarios. Top: Changing the number of input views to MATCH while keeping the number of inputs to the coarse mesh registration model (TEMPEH) at the default ( V=12 ). Bottom: Changing the number of input views for both TEMPEH and MATCH.
Figure 16: Qualitative ablation study for kT,img , i.e., the number of image tokens that each UV token attends to in the registration-guided attention blocks. The default value is kT,img=100 .
Figure 17: Top: Quantitative ablation study for the number of input views to MATCH on Ava-256. We evaluate two scenarios: i) Changing the number of input views to MATCH while keeping the number of inputs to the coarse mesh registration model (TEMPEH) at the default ( V=12 ). ii) Changing the number of input views for both TEMPEH and MATCH. Bottom: Inference speed comparison between our model with the novel registration-guided attention versus a version with dense attention across all UV and image tokens.
Figure 18: Robustness to errors in the coarse TEMPEH mesh.
kT,img
LPIPS ↓
CSIM ↑
PSNR ↑
SSIM ↑
L1 ↓
L2 ↓
25
0.184
0.926
22.908
0.830
0.032
0.009
50
0.183
0.924
23.164
0.830
0.031
0.008
100
0.187
0.918
23.032
0.825
0.032
0.009
150
0.185
0.919
22.951
0.826
0.032
0.009
Appendix
Table 8: Quantitative ablation study for kT,img , i.e., the number of image tokens that each UV token attends to in the registration-guided attention blocks. The evaluations were performed on the Ava-256 dataset. The default value is kT,img=100 .
Figure 19: In-the-wild (top) and single-image (bottom) results.
Corresp. Dist. [mm] ↓
Full Head
Face
Ears
Eyes
Mouth
Scalp
TEMPEH [ 5 ] / Ours
8.9 / 8.0
5.4 / 4.8
10.8 / 9.1
2.5 / 2.1
2.8 / 2.7
13.5 / 12.5
Appendix
Table 9: Quantitative correspondence evaluation.
Figure 20: Additional expression transfer results. Note that we only aim to transfer the oral expression and do not apply any modifications to other regions, e.g., the eyes.
Figure 21: Procedure to create subject-specific head avatars from a sequence of Gaussian splat textures predicted by MATCH.
Table 10: Head avatar reconstruction time breakdown. The measurements were conducted on a representative training sequence with 3,212 frames.
Figure 22: Qualitative ablation study of the changes applied to GEM [ 70 ] to create subject-specific head avatars from MATCH’s predictions. Ours uses 150 PCA components.
Self-Reenactment
Cross-Reenactment
LPIPS ↓
CSIM ↑
SSIM ↑
L1 ↓
PSNR ↑
CSIM ↑
EmoL1 ↓
Modality-Specific PCAs
0.180
0.879
0.816
0.027
24.376
0.815
9.849
Dynamic Mouth
0.174
0.878
0.809
0.027
24.112
0.811
9.792
# PCA Comp. = 50
0.177
0.876
0.808
0.027
24.126
0.814
9.891
# PCA Comp. = 100
0.175
0.880
0.809
0.027
24.112
0.814
9.907
Ours
0.174
0.880
0.809
0.027
24.122
0.813
9.837
Appendix
Table 11: Quantitative ablation study of the changes applied to GEM [ 70 ] to create subject-specific head avatars from MATCH’s predictions. By default, we use 150 PCA components.
We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more reference portrait images. Unlike existing feed-forward approaches that require a fixed number of input views, FFAvatar supports incremental reconstruction, progressively refining the avatar representation as additional reference images become available. At the core of our method is an alternating attention mechanism that disentangles identity appearance from expression and viewpoint variations, enabling the reconstruction of a canonical 3D appearance that remains consistent across poses and facial expressions. To balance visual fidelity and computational efficiency, we introduce a sparse-to-dense learning paradigm. Coarse appearance features are first learned using sparse primitives anchored to the FLAME vertex level and are subsequently densified in the UV domain to capture fine-grained geometric and texture details. We further propose a plug-and-play motion refinement module that enables subject-specific dynamic personalization by modeling residual motion beyond parametric deformation. Extensive experiments demonstrate that FFAvatar efficiently produces high-fidelity and controllable 4D head avatars, achieving superior flexibility, driving efficiency, and identity-consistent rendering across diverse expressions and viewpoints.
Jianjiang Yao, Ke Xian, Renxiang Dai +1
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
High-fidelity 3D Gaussian head avatar generation is critical for applications such as AR/VR, telepresence, and digital humans. Existing methods depend on multi-view datasets, 3D captures, or intermediate 2D view synthesis. In contrast, we learn both conditional and unconditional 3D head models from randomly sampled 2D images alone, without using multi-view data, 3D supervision, or intermediate view generation. We introduce MVCHead, a single-shot state space model that enforces multi-view consistency (MVC) directly in the 3D representation while regressing 3D Gaussians under these constraints. At its core, we propose a Hierarchical State Space (HiSS) block that progressively refines Gaussians from coarse to fine, while capturing long-range dependencies. Within each HiSS block, we modify Mamba's standard unidirectional scan with the proposed Hierarchical Bi-directional State Scan (HiBiSS) that aligns recurrence with the axes along which multi-view inconsistencies are strongest. Finally, we design an SE(3) Multi-view Critic that judges whether a set of self-renders arises from a single underlying 3D configuration, rewarding cross-view pixel alignment without observing real multi-view pairs. MVCHead achieves state-of-the-art perceptual quality, surpasses prior methods in both texture and geometric consistency, and maintains comparable shape consistency. To demonstrate scalability, we release FaceGS-10K, the first large-scale dataset of ready-to-use 3D Gaussian head assets for training and evaluation of 3D head models. Project Page and code: https://humansensinglab.github.io/MVCHead/
Avatar reconstruction has traditionally relied on per-subject optimization that requires hours of computation or on expensive preprocessing that limits scalability. We introduce FFAvatar, a generalizable feed-forward framework that reconstructs high-quality, animatable 3D Gaussian head avatars from few-shot unposed portrait images in seconds. FFAvatar fuses information from multiple source images into a unified canonical Gaussian representation through Multi-View Query-Former, which is animated via FLAME parameters predicted end-to-end directly from pixels, eliminating the overhead of offline FLAME extraction. We further propose a three-stage training curriculum that achieves both broad generalization and high-fidelity reconstruction: (i) scalable pretraining on extensive monocular video data with over 1M identities to learn strong generalizable priors; (ii) multi-view fine-tuning on a small but high-quality dataset of 360-degree captures to enhance geometric fidelity and extreme-view awareness; and (iii) optional personalization that adapts to specific identities for maximum fidelity within 500 optimization steps. Extensive experiments demonstrate that FFAvatar sets a new standard for identity preservation, geometric consistency, and animation fidelity. On the NeRSemble benchmark, it outperforms the state-of-the-art LAM by a substantial 5.5 PSNR gain. Furthermore, FFAvatar enables real-time deployment, reconstructing avatars in 2 seconds without personalization and 10 seconds with personalization, while supporting 49 FPS animation on a single NVIDIA A100 GPU.
Thuan Hoang Nguyen, Jiahao Luo, Yinyu Nie +3
Snap Inc. · MBZUAI · University of California, Santa Cruz