FaceCam: Portrait Video Camera Control via Scale-Aware Conditioning
Weijie Lyu Ming-Hsuan Yang Zhixin Shu
作者信息
Abstract
We introduce FaceCam, a system that generates video under customizable camera trajectories for monocular human portrait video input. Recent camera control approaches based on large video-generation models have shown promising progress but often exhibit geometric distortions and visual artifacts on portrait videos due to scale-ambiguous camera representations or 3D reconstruction errors. To overcome these limitations, we propose a face-tailored scale-aware representation for camera transformations that provides deterministic conditioning without relying on 3D priors. We train a video generation model on both multi-view studio captures and in-the-wild monocular videos, and introduce two camera-control data generation strategies: synthetic camera motion and multi-shot stitching, to exploit stationary training cameras while generalizing to dynamic, continuous camera trajectories at inference time. Experiments on Ava-256 dataset and diverse in-the-wild videos demonstrate that FaceCam achieves superior performance in camera controllability, visual quality, identity and motion preservation.引用本文复制引用
Weijie Lyu,Ming-Hsuan Yang,Zhixin Shu.FaceCam: Portrait Video Camera Control via Scale-Aware Conditioning[EB/OL].(2026-03-05)[2026-03-07].https://arxiv.org/abs/2603.05506.学科分类
计算技术、计算机技术/电子技术应用
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