Learning to Stabilize Faces

Chenglei Wu; Daoye Wang; Erroll Wood; Jan Bednarik; Thabo Beeler; Timo Bolkart; Vasileios Choutas

REVIEW 3 major objections 5 minor 37 references

Learning to Stabilize Faces

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A network trained only on synthetic 3DMM faces predicts the skull-aligning transform between two real registered face meshes, outperforming prior automatic stabilizers.

desk verdict The regression idea is new and the manual-teeth benchmark is a real strength, but the same-database 3DMM training leaves the synthetic-to-real generalization claim unproven. read the letter →

arxiv 2411.15074 v1 pith:QI4MJZKC submitted 2024-11-22 cs.CV cs.LG

Jan Bednarik , Erroll Wood , Vasileios Choutas , Timo Bolkart , Daoye Wang , Chenglei Wu , Thabo Beeler This is my paper · ORCID

classification cs.CVcs.LG

keywords facestabilizationrigidheadmotionremoval3DMorphableModelsynthetictrainingdataregisteredmeshesskullalignmentneuralnetworkregressionfacialexpressioncapture

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Face-mesh stabilization is normally a manual or heuristic-heavy step: an artist or an optimization aligns scans so that the skull stops moving and only expressions remain. This paper argues that the task reduces to regression: given two registered meshes of the same subject, a neural network directly outputs the rigid transform that brings the underlying skulls into alignment. The network is trained exclusively on synthetic pairs generated from a 3D Morphable Model whose expression basis moves the skin while leaving the skull fixed, so ground-truth transforms are known for free. On a real test set, the method reports a face-region mean vertex error of 1.08 mm, below the 1.20 mm of the best prior baseline, and it works on arbitrary expression pairs without temporal ordering. If this transfer from synthetic to real holds, stabilization becomes a fast, fully automatic preprocessing step for face-rig building, deformation transfer, and 3DMM construction.

What carries the argument

The key object is a 3D Morphable Model with a stable skull: by construction, changes in the expression parameters $\varphi$ move the exterior vertex positions $V$ but leave the hypothetical underlying skull $W$ unchanged, so for any synthetic pair the rigid transform that aligns the skulls equals the transform applied to the target mesh. This turns the unobservable skull-alignment energy $\|SW_s - W_t\|_F$ into a computable regression target and lets the authors generate unlimited training pairs by sampling identities, expressions, and random rigid perturbations (Algorithm 1). The network itself is a two-stage MLP: a shared feature extractor $F$ maps each mesh to a latent code, and a regressor $R$ turns the concatenated codes into a 6D rotation representation and a translation vector, trained with rotation-plus-translation loss.

What would settle it

Retrain the 3DMM identity PCA on a split that provably excludes the 15 test subjects, regenerate the synthetic training pairs from that held-out PCA, and re-measure the face-region mean vertex error on the same test set; if the error rises well above the reported 1.08 mm, the claimed cross-subject generalization is partly an artifact of identity leakage inside the 3DMM rather than genuine transfer.

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Extended reading notes

Core claim

The paper's central claim is that stabilization of registered face meshes can be cast as direct regression: a network consumes two meshes of the same subject in arbitrary expressions and outputs the rotation and translation that bring the underlying skulls into alignment. All training supervision comes from synthetic pairs sampled from a 3D Morphable Model with a stable skull by design—changing expression parameters deforms the exterior vertices but leaves the skull unchanged, so the alignment transform is known exactly. On a manually stabilized test set from the same capture pipeline, the network reports a face-region mean vertex error of $1.08 \pm 0.64$ mm, compared with $1.20 \pm 0.86$ mm for the strongest baseline, and a higher area under the PCK curve (78.03% versus 75.71%). The authors present this as evidence that the synthetic-data domain gap is manageable and that fully automatic, pair-based stabilization is achievable without manual skull initialization or temporal ordering.

Load-bearing premise

The load-bearing premise is that a network trained only on synthetic face pairs sampled from a 3D Morphable Model will transfer to real registered face meshes, with the upper teeth serving as a valid rigid stand-in for the skull.

Editorial extensions

If this is right

Stabilization no longer needs temporally ordered input: any two meshes of the same subject, even from different sessions or from a discrete set of expressions, can be aligned in one forward pass.
Because pairs are processed independently, large capture databases can be stabilized in parallel; the paper reports stabilizing a 1000-frame performance in roughly 6 seconds on an A100 GPU.
The synthetic-data training procedure removes the need for manual ground-truth stabilization when building the predictor, so scaling to new identities and expressions only requires sampling the 3DMM.
The method is scoped to registered meshes with a common topology in studio capture pipelines; raw in-the-wild scans are explicitly outside its intended use.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the transfer result holds across capture pipelines, the same recipe—a parametric model with a stable internal skeleton generating known ground-truth rigid transforms—could be applied to other stabilization problems, such as body scans with a fixed bone structure or animal faces.
Because the method is agnostic to pair order, a single forward pass could also serve as a fast initialization for existing optimization-based stabilization or jaw-tracking methods, potentially improving their convergence.
A natural robustness check the paper does not explicitly run is retraining with the test identities excluded from the identity PCA; that would separate genuine synthetic-to-real transfer from hidden overlap between the synthetic distribution and the test subjects.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a well-engineered method with a genuinely new formulation, and the evaluation is more honest than most in this subfield. But there's a load-bearing gap in the generalization story: the evaluation subjects are drawn from the same database used to build the 3DMM, and the paper never says they were excluded from the identity PCA. That missing detail has to be settled before the reported margin over baselines can be trusted.

What's new: treating stabilization as a direct regression from two exterior meshes to the rigid transform that aligns the underlying skulls, trained with synthetic labels from a 3DMM, is a real contribution. The method is simple, fully automatic, doesn't need temporal ordering, and handles arbitrary expression pairs. The manual-teeth benchmark is a genuine external check rather than self-reported synthetic metrics, and the ablation study (input region, dataset size, loss weights) is useful and clearly reported.

Where it's soft: first, the identity PCA issue. Section 3.2 builds the identity basis from 38360 frames of 2519 subjects. Section 4.1 picks 15 subjects from that same dataset for manual annotation and says only that their expressions were removed from the synthetic training pool. Removing expressions doesn't remove those subjects' identities from the PCA. Since training pairs sample identities from a normal distribution fit to that database, the generator can produce synthetic identities arbitrarily close to the test identities. That could artificially shrink the domain gap and inflate the 1.08 vs 1.20 mm advantage. This is a concrete, fixable problem: rerun with a leave-subjects-out identity PCA or at least state clearly that it was done. Second, the CMAP baseline is a heavily modified reimplementation tuned on the validation set; the paper acknowledges this, but it still makes the 'beats state-of-the-art' claim less crisp. Third, the test set is 20 expressions with no significance tests; the margins are within plausible noise. Fourth, the 'first learning-based method' phrasing is an overstatement given [WSS18] and [CCWL18] include learned components, though their overall approaches are different. No code or data is released, which limits reproducibility.

None of these kill the paper. The core idea is sound and the method clearly works on real meshes in the qualitative results. But the generalization claim needs the PCA exclusion confirmed or corrected. I'd send it to peer review; it deserves careful referee time. If the authors can't show exclusion, the same-database evaluation becomes a serious weakness rather than a minor one.

Referee Report

3 major / 5 minor

Summary. The paper proposes a fully automatic, learning-based method for rigidly stabilizing registered face meshes. The central idea is to train a network to predict the rigid transform between two meshes of the same subject that aligns the underlying skulls. Training pairs are synthesized with a 3DMM whose expression parameters leave the skull stable, and the network is evaluated on real meshes using manually annotated upper teeth as a rigid proxy for the skull. The method is compared against Procrustes variants, 3DMM unposing, and a reimplemented confidence-map baseline, reporting the lowest mean vertex error (1.08 mm on the face region).

Significance. If the synthetic-to-real generalization claim holds, the paper makes a useful practical contribution: it removes the need for manual stabilization, works on arbitrary expression pairs without temporal ordering, and can be parallelized for large datasets. The evaluation is more trustworthy than many papers in this area because the ground truth comes from an independent manual-teeth benchmark rather than synthetic metrics, and because the training-data synthesis scheme is described in enough detail to be reproduced. The main caveat is that the evaluation subjects are drawn from the same proprietary capture database used to build the 3DMM, and the paper does not state whether those subjects were excluded from the identity PCA; this is a load-bearing issue for the generalization claim.

major comments (3)

[§3.2, §4.1] Section 3.2 states that the vertex identity basis I is computed by PCA on a dataset of 38,360 frames of 2,519 subjects, and Section 4.1 evaluates on 15 subjects selected from this same dataset. Section 4.1 only removes 'all the expressions of the 15 subjects' from the training data; it does not state that these subjects' identities were excluded from the PCA or from the identity distribution N(βμ,Diag(βσ)) used in Section 3.3. If the test identities participate in the PCA, the synthetic training distribution includes identity directions that are close to the test subjects, so the reported face-region error of 1.08±0.64 mm in Table 1 may partly reflect identity-specific skin-to-skull geometry seen during training rather than generalization to unseen identities. Please state explicitly whether the 15 evaluation subjects were excluded from the identity PCA and from the identity statistics; if not, retrain the 3DMM and the network leaving out all data of those subjects and rerun the full comparison in Table 1. This is necessary to support the synthetic-to-real generalization claim made in §3.3 and §4.4.
[Table 1, §4.4] Table 1 reports a mean vertex distance of 1.08±0.64 mm for the proposed method versus 1.20±0.86 mm for CMAP on the face region, with only 20 test expression pairs. No per-subject or per-pair error distributions, confidence intervals, or significance tests are reported, and the mx and mAUC columns are point estimates without error bars. Given the overlapping standard deviations and the small test set, the headline '10% improvement' over CMAP is not shown to be statistically reliable. Please report paired significance tests (e.g., Wilcoxon signed-rank or bootstrapped confidence intervals) on all three metrics, and clarify the number of subjects and expression pairs behind each aggregate number.
[§4.3, Appendix §2] The CMAP baseline is not the published method of [WSS18]. Section 4.3 states that the original formulation produced unsatisfactory results 'on par with the basic PROChead', and the appendix describes a reimplementation with two additional energy terms (contrast and spatial consistency) whose hyperparameters are tuned on the validation set. The variant labeled CMAP in Table 1 and Fig. 7 is therefore a substantially strengthened reimplementation, so the claim of outperforming prior work is not a comparison with the actual state of the art. Please compare with the original formulation as published (or with the authors' code, if available), or clearly label the baseline as a modified reimplementation and adjust the claim accordingly.

minor comments (5)

[Eq. (2)] The domain notation R^{2×4×NV} is imprecise: S takes a pair of matrices, each in R^{4×NV}. Please rewrite the function signature as S : R^{4×NV} × R^{4×NV} → R^{4×4}.
[References] The reference [Gow75] appears to be a copy-paste error: the 1975 Psychometrika paper is 'Generalized Procrustes analysis', not 'Face transfer with multilinear models', which is properly the title of [VBPP05].
[§4.1] The description of the evaluation data is confusing: the text mentions 45 expressions, one annotated expression per subject, 30 source frames, and then a split into 10 validation and 20 test expressions. Please clarify the exact pairing and splitting procedure (e.g., 15 subjects × 3 expressions, with 2 target expressions per annotated source).
[Table 1, §4.3] Method names are inconsistent between the text and the table: 'PROC head' versus 'PROChead' and 'UNPOSE id' versus 'UNPOSEid' should be made uniform.
[§3.3, §3.4] The free hyperparameters αT, εR, εT, and εφ are set empirically without a sensitivity analysis. A short study or at least a statement of their typical effect on the validation error would increase confidence in the robustness of the reported results.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: synthetic training labels are generated from the 3DMM's stable-skull construction, while test labels are independent manual teeth annotations; no prediction reduces to a fitted input or self-citation.

full rationale

The derivation chain is self-contained. The network is trained with labels S generated in Algorithm 1 from the 3DMM's stable-skull property: for fixed identity beta, changing expression phi leaves the implied skull fixed, so the applied rigid transform is the minimizer of Eq. (1) by Eq. (3). This is a definitional construction of training data, not a circular prediction. At test time, ground truth comes from an external protocol: manual 2D keypoints on the upper teeth triangulated to a 3D polyline (Sec. 4.1), a rigid proxy for the skull that does not depend on the network or the 3DMM's identity basis. The comparison baselines are either standard Procrustes variants or a reimplemented WSS18 module tuned on the validation set; no baseline is defined in terms of the paper's fitted parameters. The only self-citations (BB14 for evaluation protocol, WBH21 for the 3DMM formulation) are used as ordinary prior work, not as load-bearing uniqueness theorems, and the 3DMM's construction is described in the paper. The reviewer-flagged concern that the 15 evaluation subjects may not have been excluded from the identity PCA is a data-leakage/generalization risk, not a circularity: the predicted rigid transform is not defined as, or statistically forced to be, a function of the identity basis, and the test errors are measured against manual teeth annotations. Correctness risk should be assessed separately; it does not change the circularity score.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No new physical or abstract entities are postulated. The 3DMM is a data-driven model assembled from existing blendshapes and PCA, not a new entity. The central claim rests on free hyperparameters (loss weight, noise scales) and domain assumptions about the stability of the skull and the representativeness of synthetic data.

free parameters (4)

translation loss weight alpha_T = 1
Empirically set in Eq. 5 (Sec 3.4); balances rotation and translation loss terms.
pose noise scale epsilon_R = not reported
Standard deviation for sampling random rotation angles in Appendix 1; controls synthetic misalignment diversity.
pose noise scale epsilon_T = not reported
Standard deviation for sampling random translations in Appendix 1; controls synthetic misalignment diversity.
expression noise scale epsilon_phi = not reported
Perturbation added to sampled expression vectors in Algorithm 1; increases synthetic expression diversity.

assumptions (4)

domain assumption The 3DMM has a stable skull: changing expression parameters phi does not move the underlying skull.
Invoked in Sec 3.2 and Eq. 3 to generate ground-truth rigid transforms for synthetic training pairs. The authors argue it holds by design of the blendshapes, but real skin-skull sliding is not modeled.
domain assumption Upper teeth are rigidly attached to the skull and can serve as a ground-truth proxy for skull alignment.
Used in Sec 4.1 to create the manual evaluation benchmark. It limits evaluation to expressions with visible upper teeth.
domain assumption Registered meshes are in dense correspondence with a common template topology.
The network consumes flattened vertex coordinates, so fixed topology is required (Sec 3.1 and Sec 3.3 preprocessing).
ad hoc to paper The synthetic sampling distribution, Gaussian identities plus random sampled expressions and small pose noise, approximates the real distribution of registered face pairs.
Algorithm 1 defines this distribution; the paper's generalization claim depends on this approximation, validated only on a small same-studio test set.

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Cite this review

Pith. "Pith review of Learning to Stabilize Faces." pith.science (2026). https://pith.science/paper/QI4MJZKC

@misc{pith2026241115074,
  author       = {Pith},
  title        = {Pith review of: Learning to Stabilize Faces},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QI4MJZKC}},
  note         = {Machine review of arXiv:2411.15074}
}

read the original abstract

Nowadays, it is possible to scan faces and automatically register them with high quality. However, the resulting face meshes often need further processing: we need to stabilize them to remove unwanted head movement. Stabilization is important for tasks like game development or movie making which require facial expressions to be cleanly separated from rigid head motion. Since manual stabilization is labor-intensive, there have been attempts to automate it. However, previous methods remain impractical: they either still require some manual input, produce imprecise alignments, rely on dubious heuristics and slow optimization, or assume a temporally ordered input. Instead, we present a new learning-based approach that is simple and fully automatic. We treat stabilization as a regression problem: given two face meshes, our network directly predicts the rigid transform between them that brings their skulls into alignment. We generate synthetic training data using a 3D Morphable Model (3DMM), exploiting the fact that 3DMM parameters separate skull motion from facial skin motion. Through extensive experiments we show that our approach outperforms the state-of-the-art both quantitatively and qualitatively on the tasks of stabilizing discrete sets of facial expressions as well as dynamic facial performances. Furthermore, we provide an ablation study detailing the design choices and best practices to help others adopt our approach for their own uses. Supplementary videos can be found on the project webpage syntec-research.github.io/FaceStab.

Figures

Figures reproduced from arXiv: 2411.15074 by the authors.

**Figure 1.** Our method stabilizes face meshes using machine learning. Given an input pair of face meshes, we predict the rigid transform between the two, and remove it. Shown on the right are all face meshes overlaid. After stabilization, the rigid head parts appear aligned. Abstract Nowadays, it is possible to scan faces and automatically register them with high quality. However, the resulting face meshes often need further pr… view at source ↗

**Figure 2.** Our dataset includes 38360 face meshes which were registered to multi-view images. These registrations are used for building our 3DMM and sampling random expressions. Head stabilization is often addressed as a step within face reconstruction and tracking pipelines [FNH∗ 17, WSS18, CCWL18]. The method of [FNH∗ 17] initializes the stabilization using Procrustes alignment, followed by finding axis-aligned rotations o… view at source ↗

**Figure 3.** The architecture of the rigid transformation predictor. The network takes a pair (Vs,Vt) of source and target skin vertices of the same subject on the input and predicts the rotation R and translation t which best aligns the input pair on the output. 3.1. Problem Formulation Our goal is to remove unwanted rigid transformation between any two head meshes from a single subject so that the underlying skulls are aligned… view at source ↗

Figures from the paper (8 more)

**Figure 4.** Figure 4: Our stabilization neural network is trained with synthetic data only. We synthesize realistic and diverse faces by mixing random identities (Fig. 4a) with random expressions (Fig. 4b). dataset contains 38360 frames of 2519 subjects performing a variety of different ex…

**Figure 5.** Figure 5: Schematic view of the kinematic chain of our 3DMM (left) and skinning weights corresponding to the joints (right). 3DMMs have a stable skull. Generally, 3DMMs do not explicitly model the skull. But, by design, changing expression parameters φ should only deform the e…

**Figure 6.** Figure 6: The stabilization error visualized as an error map in the range [0,3] mm. Loss functions. Since we know the GT transformation Si for each source-target pair, we train the transformation predictor in a supervised way with the following loss function: L = LR +αT LT , (5…

**Figure 7.** Figure 7: Percentage of correct keypoints (PCK) computed on three different head regions, Head, Face and U pper shown for all the tested methods. 3.4. Training Details The feature extractor F is modeled as a 3-layered Multilayer Perceptron (MLP) with sizes (1024,512,512), the d…

**Figure 8.** Figure 8: Visual comparison of the methods showing predicted (green) and GT (blue) 3D teeth line projected to the front and side views [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

**Figure 9.** Figure 9: Impact of the size of the training set on the performance of our method. Training dataset size. As discussed in Sec. 3.3, a 3DMM allows us to generate a dataset of unlimited size. How big a dataset is necessary to reach a good performance? We trained OUR on the rando…

**Figure 10.** Figure 10: Distribution of the facial mask weights learned by the various versions of CMAP. Original Contrast Contrast&Consistent 0 1 [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]

**Figure 11.** Figure 11: Facial masks learned by the various versions of CMAP. Then, CMAP finds the optimal weights w ∗ by solving the following minimization problem: w ∗ = argmin w αdataLdata +αregLreg Ldata = ∥P(U˜ s,U˜ t)−U˜ t∥ 2 F Lreg = max 0,ρN − ∥w∥ 2 , where αdata,αreg are loss ter…

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

write newline

" write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTIO...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION fin.entry.original add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 i...

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S., Huang T

Arun K. S., Huang T. S., Blostein S. D. : Least-squares fitting of two 3-d point sets. IEEE Transactions on pattern analysis and machine intelligence, 5 (1987), 698--700

work page 1987

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J., Debevec P

Alexander O., Rogers M., Lambeth W., Chiang M. J., Debevec P. E. : The digital Emily project: photoreal facial modeling and animation. In SIGGRAPH Courses (2009), pp. 12:1--12:15

work page 2009

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: Rigid stabilization of facial expressions

Beeler T., Bradley D. : Rigid stabilization of facial expressions. SIGGRAPH (2014)

work page 2014

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Bogo F., Kanazawa A., Lassner C., Gehler P., Romero J., Black M. J. : Keep it SMPL : Automatic estimation of 3D human pose and shape from a single image. In European Conference on Computer Vision (2016)

work page 2016

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Bolkart T., Li T., Black M. J. : Instant multi-view head capture through learnable registration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)

work page 2023

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: Online modeling for realtime facial animation

Bouaziz S., Wang Y., Pauly M. : Online modeling for realtime facial animation. ACM Transactions on Graphics (2013)

work page 2013