[Paper] Face Anything: 4D Face Reconstruction from Any Image Sequence

Source: arXiv - 2604.19702v1

Overview

The paper “Face Anything: 4D Face Reconstruction from Any Image Sequence” introduces a single, feed‑forward neural network that can turn any collection of photos or video frames of a person into a temporally coherent, high‑resolution 3‑D face model that moves over time (i.e., a 4‑D reconstruction). By predicting a canonical facial coordinate for every pixel together with depth, the authors collapse the notoriously hard problems of dense tracking and dynamic reconstruction into a single, unified task.

Key Contributions

• Canonical facial point prediction: Each pixel is mapped to a normalized coordinate in a shared “canonical” face space, providing a stable reference across frames.
• Joint depth‑and‑canonical prediction transformer: A single transformer‑based architecture simultaneously outputs per‑pixel depth and canonical coordinates, eliminating the need for separate tracking or fitting stages.
• Fully feed‑forward pipeline: No iterative optimization at test time; the model runs in a single forward pass, delivering real‑time speeds.
• State‑of‑the‑art accuracy: 3× lower correspondence error and 16% better depth quality than previous dynamic reconstruction methods.
• Broad applicability: Works on arbitrary image sequences (single‑view video, multi‑view photo bursts, even low‑quality webcam footage).

Methodology

Canonical Space Definition

• A neutral, front‑facing 3‑D face mesh is chosen as the canonical reference.
• Every point on a real face is expressed as a normalized 2‑D coordinate (u, v) in this space, regardless of pose or expression.

Network Architecture

• A Vision Transformer (ViT) backbone processes each input frame.
• Two heads branch out: one predicts a dense depth map, the other predicts the (u, v) canonical coordinates for every pixel.
• The two predictions are fused internally, allowing the model to reason jointly about geometry (depth) and correspondence (canonical mapping).

Training Strategy

• Synthetic multi‑view data is generated by non‑rigidly warping a high‑quality 3‑D face model into many poses and expressions.
• Ground‑truth depth and canonical coordinates are known for each warped view, providing supervision.
• A multi‑task loss combines depth regression, canonical coordinate classification, and a smoothness regularizer to encourage coherent surfaces.

Inference & Reconstruction

• For a sequence of frames, the model outputs per‑frame depth + canonical maps.
• Because the canonical map is consistent across time, points can be directly linked frame‑to‑frame, yielding a dense, temporally stable 4‑D mesh without any post‑hoc tracking.

Results & Findings

• Benchmarks: Tested on the BU‑4DFE video dataset, VoxCeleb‑2 video clips, and a custom multi‑view photo burst collection.
• Qualitative: The reconstructed meshes preserve fine expression details (e.g., subtle eyebrow raises) while staying stable across rapid head turns.
• Ablation: Removing the canonical head degrades correspondence accuracy dramatically, confirming its central role.

Practical Implications

• Real‑time avatar creation: Game engines and virtual‑reality platforms can generate lifelike, animated face avatars on‑the‑fly from a webcam feed, without expensive offline fitting.
• Facial animation pipelines: Studios can replace multi‑camera rigs with a single camera and still obtain dense, temporally consistent geometry for performance capture.
• Telepresence & AR filters: Apps can apply high‑fidelity 3‑D effects (e.g., realistic masks, makeup) that stay locked to the user’s face even during rapid motion.
• Security & biometrics: Accurate 4‑D reconstructions improve spoof detection by analyzing subtle depth and motion cues that 2‑D images lack.
• Healthcare: Non‑invasive monitoring of facial muscle dynamics for speech therapy or neurological assessment becomes feasible with just a phone camera.

Limitations & Future Work

• Training data bias: The model is trained on synthetic deformations of a limited set of base face meshes, which may limit generalization to extreme ethnic diversity or atypical facial structures.
• Occlusions: Heavy occlusion (e.g., hands covering the face) still leads to gaps in the reconstruction; the current pipeline does not explicitly model occlusion reasoning.
• Fine‑scale skin detail: While geometry is accurate, micro‑texture (e.g., pores, wrinkles) is not captured; integrating a high‑frequency texture branch is a natural next step.
• Temporal consistency beyond per‑frame: Although the canonical map enforces correspondence, occasional jitter can appear in very fast motions; a lightweight temporal smoothing module could further stabilize results.

Bottom line: By turning dense facial tracking into a canonical coordinate prediction problem, the authors deliver a fast, accurate, and developer‑friendly solution for 4‑D face reconstruction—opening the door to a new wave of real‑time, geometry‑aware facial applications.

Authors

• Umut Kocasari
• Simon Giebenhain
• Richard Shaw
• Matthias Nießner

Paper Information

• arXiv ID: 2604.19702v1
• Categories: cs.CV
• Published: April 21, 2026
• PDF: Download PDF