3D Avatar Generation
Overview
3D avatar generation studies how to build digital humans that preserve a person's appearance and can be viewed or animated from new poses and viewpoints. Methods use parametric models such as FLAME and SMPL-X, neural fields and 3D Gaussian splatting, and increasingly diffusion or transformer models for feed-forward reconstruction from a single image, sparse portraits, or video. Research also targets controllable facial and body motion, speech-driven expression, and realistic clothing and hair, with evaluation on benchmarks such as NeRSemble, ZJU-MoCap, and 4D-DRESS. A central challenge is balancing identity fidelity and 3D consistency with broad generalization, realistic dynamics, and fast inference, especially when parts of the person are unseen or training data are limited.
Latest papers
90 papers11 papers in the last four weeks, up 120% on the four weeks before. 0.1% of all new papers.