mlsanityData to 14 Oct 2026

Head360: Learning a Parametric 3D Full-Head for Free-View Synthesis in 360deg

arXiv 2408.00296 ↗First posted 1 Aug 2024cs.CVcited by 9

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Abstract (arXiv)

Creating a 360deg parametric model of a human head is a very challenging task. While recent advancements have demonstrated the efficacy of leveraging synthetic data for building such parametric head models, their performance remains inadequate in crucial areas such as expression-driven animation, hairstyle editing, and text-based modifications. In this paper, we build a dataset of artist-designed high-fidelity human heads and propose to create a novel parametric 360deg renderable parametric head model from it. Our scheme decouples the facial motion/shape and facial appearance, which are represented by a classic parametric 3D mesh model and an attached neural texture, respectively. We further propose a training method for decompositing hairstyle and facial appearance, allowing free-swapping of the hairstyle. A novel inversion fitting method is presented based on single image input with high generalization and fidelity. To the best of our knowledge, our model is the first parametric 3D full-head that achieves 360deg free-view synthesis, image-based fitting, appearance editing, and animation within a single model. Experiments show that facial motions and appearances are well disentangled in the parametric space, leading to SOTA performance in rendering and animating quality. The code and SynHead100 dataset are released at https://nju-3dv.github.io/projects/Head360.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 46 of its 71 references that are in the map. First posted on arXiv 1 Aug 2024.

Builds on

46 of 71 references.

References · 46

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34 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 2.

9 papers.