让单视角人脸视频生成稳定可控的动态镜头，解决畸变问题。
FaceCam: Portrait Video Camera Control via Scale-Aware Conditioning
- 用专为人脸设计的尺度感知相机表示，避免依赖3D先验
- 在Ava-256等数据集上实现高质量、身份一致的镜头控制
- 适合需要自然镜头运动的人像视频生成场景
我们提出FaceCam系统，可基于单视角人像视频生成自定义相机轨迹的视频。现有基于大模型的相机控制方法常因尺度模糊或3D重建误差，在人像视频中产生几何失真和视觉伪影。为此，我们设计了一种面向人脸的尺度感知相机变换表示，实现确定性条件生成，无需依赖3D先验。模型在多视角棚拍数据与真实环境单视角视频上联合训练，并引入合成相机运动与多帧拼接两种数据生成策略，使固定训练相机能泛化至推理时的动态连续轨迹。在Ava-256数据集及多样真实视频上的实验表明，FaceCam在相机可控性、视觉质量、身份与动作保持方面均表现优异。
原文摘要 · Abstract (English)
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.
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