{"paper":{"title":"FFAvatar: Few-Shot, Feed-Forward, and Generalizable Avatar Reconstruction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A feed-forward model reconstructs animatable 3D Gaussian head avatars from few unposed photos in seconds without per-subject optimization.","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.GR","authors_text":"Gordon Guocheng Qian, Hao Li, Jiahao Luo, Jian Wang, Thuan Hoang Nguyen, Yinyu Nie","submitted_at":"2026-05-14T18:33:49Z","abstract_excerpt":"Avatar reconstruction has traditionally relied on per-subject optimization that requires hours of computation or on expensive preprocessing that limits scalability. We introduce FFAvatar, a generalizable feed-forward framework that reconstructs high-quality, animatable 3D Gaussian head avatars from few-shot unposed portrait images in seconds. FFAvatar fuses information from multiple source images into a unified canonical Gaussian representation through Multi-View Query-Former, which is animated via FLAME parameters predicted end-to-end directly from pixels, eliminating the overhead of offline "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"FFAvatar reconstructs high-quality, animatable 3D Gaussian head avatars from few-shot unposed portrait images in seconds and sets a new standard for identity preservation, geometric consistency, and animation fidelity, outperforming the state-of-the-art LAM by a substantial 5.5 PSNR gain on the NeRSemble benchmark.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The three-stage training curriculum on monocular video data with over 1M identities followed by multi-view fine-tuning produces priors that generalize to arbitrary few-shot unposed inputs without requiring offline pose or FLAME extraction.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"FFAvatar is a generalizable feed-forward framework that reconstructs high-quality animatable 3D Gaussian head avatars from few-shot unposed portrait images in seconds via Multi-View Query-Former and end-to-end FLAME prediction.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A feed-forward model reconstructs animatable 3D Gaussian head avatars from few unposed photos in seconds without per-subject 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