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arxiv: 2604.19702 · v1 · submitted 2026-04-21 · 💻 cs.CV

Face Anything: 4D Face Reconstruction from Any Image Sequence

Umut Kocasari , Simon Giebenhain , Richard Shaw , Matthias Nie{\ss}ner This is my paper

Pith reviewed 2026-05-10 02:35 UTC · model grok-4.3

classification 💻 cs.CV
keywords 4D face reconstructioncanonical coordinatesdepth estimationfacial point trackingtransformer modeldynamic geometrymulti-view supervisionfeed-forward reconstruction
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The pith

Canonical facial point prediction unifies depth estimation, dense 3D geometry, and point tracking for 4D face reconstruction from single-view sequences.

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

The paper establishes that assigning each pixel a normalized coordinate in a shared canonical facial space turns the ambiguous problem of non-rigid face deformation and viewpoint change into a single canonical reconstruction task. Jointly predicting these coordinates together with depth inside one transformer model produces temporally consistent geometry and reliable correspondences. Training occurs on multi-view data that is non-rigidly warped into the same canonical space, allowing the network to generalize to arbitrary image sequences without separate tracking stages or post-processing. If this holds, reconstruction and tracking become feed-forward operations that deliver lower correspondence error and improved depth accuracy compared with prior dynamic methods.

Core claim

The method formulates high-fidelity 4D facial reconstruction as canonical facial point prediction: each pixel receives a normalized facial coordinate in a shared canonical space. A transformer jointly predicts these coordinates and per-pixel depth after training on multi-view geometry data that has been non-rigidly warped into the canonical space. This single feed-forward architecture yields accurate depth, temporally stable dense 3D geometry, and robust facial point tracking on arbitrary image sequences.

What carries the argument

Canonical facial point prediction: a representation that assigns each pixel a normalized facial coordinate in a shared canonical space, converting dense tracking and dynamic reconstruction into a canonical reconstruction problem.

If this is right

  • Accurate depth estimation from single-view image sequences
  • Temporally stable reconstruction of dynamic 3D facial geometry
  • Dense 3D output together with robust facial point tracking
  • Approximately 3 times lower correspondence error and 16 percent better depth accuracy than prior dynamic reconstruction methods
  • Faster inference in a single feed-forward pass without post-processing

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The feed-forward design could support real-time video pipelines where separate optimization stages are impractical.
  • Enforcing consistency through a canonical space may reduce drift over long sequences compared with frame-by-frame methods.
  • Similar coordinate-based representations might transfer to reconstruction of other non-rigid surfaces once appropriate canonical spaces are defined.

Load-bearing premise

Multi-view geometry data can be reliably non-rigidly warped into a shared canonical space so that a model trained on it will generalize to arbitrary single-view image sequences without extra constraints or post-processing.

What would settle it

A test sequence containing rapid expression changes or large viewpoint shifts where the predicted canonical coordinates produce drifting tracks across frames or depth values that deviate measurably from ground-truth multi-view reconstructions.

Figures

Figures reproduced from arXiv: 2604.19702 by Matthias Nie{\ss}ner, Richard Shaw, Simon Giebenhain, Umut Kocasari.

Figure 1
Figure 1. Figure 1: Face Anything. Unified 4D facial reconstruction and dense track￾ing from image sequences via joint prediction of depth and canonical facial coordinates. Left to right: RGB input, 4D reconstruction with tracks, canon￾ical maps, depth maps, and normal maps. Website: https://kocasariumut. github.io/FaceAnything/ Abstract. Accurate reconstruction and tracking of dynamic human faces from image sequences is chal… view at source ↗
Figure 2
Figure 2. Figure 2: Architecture overview. Given image sequences, our method jointly predicts depth and canonical facial maps to enable dense 4D reconstruction and tracking. Dense correspondences are established in canonical space, producing temporally consistent geometry and point trajectories. Training. We train the model in two stages. First, the architecture is pre￾trained on DAViD [53] using monocular input to learn faci… view at source ↗
Figure 3
Figure 3. Figure 3: Dataset creation. We generate training supervision by combining multi-view reconstruction with parametric face tracking to produce depth maps and canonical facial maps. Although the parametric face model may not capture fine-scale geomet￾ric details, high-frequency information from COLMAP reconstruction is preserved in the canonical maps. This process provides geometrically consistent supervision across vi… view at source ↗
Figure 4
Figure 4. Figure 4: 4D reconstruction comparison on VFHQ, NeRSemble, and Ava-256. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Single-view vs multi-view depth prediction. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: 2D tracking comparison on VFHQ. Track points are defined in the base image and each method predicts trajectories to the target image that should end at the same facial locations. Our method produces more accurate and consistent corre￾spondences than recent approaches. Depth Accuracy. Depth evaluation is reported in Tab. 1 for both image￾based and video-based reconstruction settings. In the video-based sett… view at source ↗
Figure 7
Figure 7. Figure 7: 4D reconstruction and tracking comparison on CelebV-HQ. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Correspondence and temporal prediction errors on NeRSemble. [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Additional prediction examples on VFHQ. Given two input views, our method predicts depth maps and canonical maps for each frame. The results demon￾strate consistent geometry and canonical representations across different identities and expressions [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Additional canonical point cloud prediction examples on VFHQ. [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Additional predictions on VFHQ. Given two RGB input views, our method reconstructs 4D facial geometry and predicts dense correspondences via canon￾ical facial coordinates. The results demonstrate consistent geometry and correspon￾dences across different viewpoints, facial expressions, and identities [PITH_FULL_IMAGE:figures/full_fig_p026_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Failure case on VFHQ. Given two input RGB images, we visualize the pre￾dicted correspondences between the reconstructed point clouds. While the correspon￾dences are largely accurate on the facial region, the method fails on the microphone, which is not part of the facial surface and leads to incorrect matches. This highlights a limitation when non-face objects are present in the scene [PITH_FULL_IMAGE:fi… view at source ↗
read the original abstract

Accurate reconstruction and tracking of dynamic human faces from image sequences is challenging because non-rigid deformations, expression changes, and viewpoint variations occur simultaneously, creating significant ambiguity in geometry and correspondence estimation. We present a unified method for high-fidelity 4D facial reconstruction based on canonical facial point prediction, a representation that assigns each pixel a normalized facial coordinate in a shared canonical space. This formulation transforms dense tracking and dynamic reconstruction into a canonical reconstruction problem, enabling temporally consistent geometry and reliable correspondences within a single feed-forward model. By jointly predicting depth and canonical coordinates, our method enables accurate depth estimation, temporally stable reconstruction, dense 3D geometry, and robust facial point tracking within a single architecture. We implement this formulation using a transformer-based model that jointly predicts depth and canonical facial coordinates, trained using multi-view geometry data that non-rigidly warps into the canonical space. Extensive experiments on image and video benchmarks demonstrate state-of-the-art performance across reconstruction and tracking tasks, achieving approximately 3$\times$ lower correspondence error and faster inference than prior dynamic reconstruction methods, while improving depth accuracy by 16%. These results highlight canonical facial point prediction as an effective foundation for unified feed-forward 4D facial reconstruction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a unified feed-forward method for 4D facial reconstruction from any image sequence using canonical facial point prediction. By assigning each pixel a normalized coordinate in a shared canonical space and jointly predicting depth, the approach converts dense tracking and dynamic reconstruction into a canonical problem. A transformer model is trained on multi-view geometry data non-rigidly warped into this canonical space, claiming state-of-the-art performance with approximately 3 times lower correspondence error, 16% improved depth accuracy, and faster inference compared to prior methods.

Significance. If validated, this work offers a significant advancement in dynamic face reconstruction by providing a single architecture for accurate depth estimation, temporally stable geometry, dense 3D output, and robust point tracking without post-processing. The canonical coordinate representation is a strength for handling non-rigid deformations and viewpoint variations. Credit is due for the joint prediction formulation and the emphasis on feed-forward efficiency.

major comments (2)
  1. [Method (training procedure)] The non-rigid warping of multi-view data into canonical space is central to generating training labels (described in the method section), yet no quantitative validation of the warping accuracy, residual alignment errors, or sensitivity to expression changes and occlusions is provided. Given that the model is strictly feed-forward at inference on monocular sequences, any supervision noise from imperfect warping directly impacts the claimed generalization and the reported 3× correspondence improvement.
  2. [Experiments] The abstract and results section report benchmark improvements (3× correspondence error reduction, 16% depth gain) but omit details on error bars, exact baseline implementations, data splits, ablation studies, or statistical significance tests. This absence undermines the ability to assess the robustness of the SOTA claims and the temporal stability assertions.
minor comments (2)
  1. [Abstract] The phrasing 'Face Anything' in the title and 'any image sequence' could be clarified to specify the assumptions on input quality or face visibility.
  2. [Notation] The definition of canonical facial coordinates should include an explicit equation or diagram showing how normalization is performed across different expressions and views.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the work's significance. We address each major comment below with clarifications and commit to revisions that strengthen the manuscript without misrepresenting the original contributions.

read point-by-point responses

  1. Referee: [Method (training procedure)] The non-rigid warping of multi-view data into canonical space is central to generating training labels (described in the method section), yet no quantitative validation of the warping accuracy, residual alignment errors, or sensitivity to expression changes and occlusions is provided. Given that the model is strictly feed-forward at inference on monocular sequences, any supervision noise from imperfect warping directly impacts the claimed generalization and the reported 3× correspondence improvement.

    Authors: We agree that quantitative validation of the non-rigid warping procedure would provide stronger evidence for the quality of the generated training labels. In the revised manuscript, we will add a new subsection (or supplementary material) reporting metrics such as mean residual alignment error on held-out multi-view sequences, before/after warping comparisons, and sensitivity analyses to expression changes and partial occlusions. These additions will directly support the reliability of the supervision and the generalization claims. revision: yes

  2. Referee: [Experiments] The abstract and results section report benchmark improvements (3× correspondence error reduction, 16% depth gain) but omit details on error bars, exact baseline implementations, data splits, ablation studies, or statistical significance tests. This absence undermines the ability to assess the robustness of the SOTA claims and the temporal stability assertions.

    Authors: We acknowledge that additional experimental details are necessary for full reproducibility and to rigorously substantiate the reported improvements. In the revised version, we will expand the experiments section and supplementary material to include error bars (standard deviations across runs), precise specifications of baseline implementations and data splits, further ablation studies on the joint prediction and canonical representation, and statistical significance tests (e.g., paired t-tests) for the key metrics. These changes will also address the temporal stability claims with supporting quantitative evidence. revision: yes

Circularity Check

0 steps flagged

No circularity: canonical coordinate prediction is learned from external warped multi-view data

full rationale

The paper defines canonical facial points by non-rigidly warping multi-view geometry into a shared space and trains a transformer to regress depth plus these coordinates from monocular images. This is a standard supervised mapping with no equations that reduce the predicted outputs to the training inputs by construction, no self-citations invoked as uniqueness theorems, and no fitted parameters renamed as predictions. Evaluation occurs on separate benchmarks, so the claimed gains in correspondence and depth accuracy remain independent of the derivation inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Only the abstract is available. The central claim rests on the existence of a learnable shared canonical facial space and the ability of multi-view warped data to supervise a general feed-forward model. No explicit free parameters or invented physical entities are named.

invented entities (1)
  • canonical facial coordinates no independent evidence
    purpose: normalized per-pixel coordinates in a shared space that decouples tracking from viewpoint and expression changes
    Introduced as the core new representation that turns dynamic reconstruction into a canonical prediction task.

pith-pipeline@v0.9.0 · 5521 in / 1113 out tokens · 26802 ms · 2026-05-10T02:35:12.216586+00:00 · methodology

discussion (0)

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