Face Anything: 4D Face Reconstruction from Any Image Sequence

Accurate reconstruction and tracking of dynamic human facesfrom image sequences is challenging because non-rigid deformations, ex-pression changes, and viewpoint variations occur simultaneously, creat-ing significant ambiguity in geometry and correspondence estimation. Wepresent a unified method for high-fidelity 4D facial reconstruction basedon canonical facial point prediction, a representation that assigns eachpixel a normalized facial coordinate in a shared canonical space. Thisformulation transforms dense tracking and dynamic reconstruction intoa canonical reconstruction problem, enabling temporally consistent ge-ometry and reliable correspondences within a single feed-forward model.By jointly predicting depth and canonical coordinates, our method en-ables accurate depth estimation, temporally stable reconstruction, dense3D geometry, and robust facial point tracking within a single architec-ture. We implement this formulation using a transformer-based modelthat jointly predicts depth and canonical facial coordinates, trained us-ing multi-view geometry data that non-rigidly warps into the canonicalspace. Extensive experiments on image and video benchmarks demon-strate state-of-the-art performance across reconstruction and trackingtasks, achieving approximately 3× lower correspondence error and fasterinference than prior dynamic reconstruction methods, while improvingdepth accuracy by 16%. These results highlight canonical facial point pre-diction as an effective foundation for unified feed-forward 4D facial recon-struction. Website: https://kocasariumut.github.io/FaceAnything/