cs.CVMar 5, 2026
SaveFaceCam: Portrait Video Camera Control via Scale-Aware Conditioning
Organizations: University of California, Merced · Adobe Research
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.
Figures & tables
Figure 2: Camera representation comparison. We contrast (A) parameter-based representations, which are standard in camera control methods, with (B) image-space point correspondences, which we adopt in FaceCam to obtain a scale-aware conditioning that enables precise camera control.
Figure 3: Training and inference pipeline of FaceCam .
Figure 4: Training data generation examples. The source video is applied with scale and color augmentation to increase data diversity, while the target video is augmented with all three types to train the model’s camera control capability.
| Method | PSNR | SSIM | LPIPS | ArcFace |
|---|---|---|---|---|
| ReCamMaster [ 3 ] | 9.73 | 0.5570 | 0.5809 | 0.7014 |
| TrajectoryCrafter [ 57 ] | 10.32 | 0.5462 | 0.5673 | 0.5220 |
| FaceCam * | 9.83 | 0.5816 | 0.5494 | 0.8073 |
| FaceCam | 15.85 | 0.7208 | 0.2521 | 0.8574 |
Table 1: Quantitative results on Ava-256. FaceCam outperforms the baselines on both reconstruction metrics and facial identity metric, indicating stronger stationary camera control ability and better preservation of identity and motion.
Figure 5: Qualitative results on Ava-256. FaceCam produces more realistic, ground-truth-aligned novel views than baselines. ReCamMaster [ 3 ] often fails under large pose changes, pushing the head out of frame, while TrajectoryCrafter [ 57 ] frequently shows facial distortions from dynamic point-cloud errors.
| Method | Camera Correctness | ArcFace Similarity | Imaging Quality | Aesthetic Quality | Subject Consistency | Background Consistency | Motion Smoothness | Dynamic Degree |
|---|---|---|---|---|---|---|---|---|
| ReCamMaster [ 3 ] | 83.00 | 78.92 | 69.05 | 55.85 | 93.26 | 93.02 | 99.30 | 90.00 |
| TrajectoryCrafter [ 57 ] | 99.00 | 49.79 | 71.37 | 55.76 | 92.23 | 92.25 | 98.97 | 97.00 |
| FaceCam (w/o In-the-wild Videos) | 100.00 | 77.73 | 70.71 | 55.73 | 94.52 | 95.16 | 99.23 | 89.00 |
| FaceCam | 97.00 | 83.94 | 73.49 | 59.91 | 94.77 | 94.98 | 99.05 | 96.00 |
Table 2: Quantitative results on In-the-wild videos. FaceCam demonstrates superior identity preservation and camera trajectory correctness. It also generates videos with better visual quality and consistency as evidenced by VBench [ 25 ] scores.
Figure 6: Qualitative results on in-the-wild videos. We present three camera motions: (1) Arc Left, (2) Pan Right, and (3) Zoom In. ReCamMaster [ 3 ] often loses camera control in angle changes (panel 1) and produces blurry outputs under zoom in (panel 3). TrajectoryCrafter [ 57 ] yields flattened faces with weak facial texture (panel 2). FaceCam delivers higher visual quality and trajectory correctness, and more faithfully captures human geometry, including hands, hair, and facial features.
Figure 7: In-the-wild results under diverse camera trajectories. For each example, the first row shows the source video and the target camera, and the second row shows generated video. FaceCam closely follows the specified trajectories and robustly handles everyday portrait scenarios: it synthesizes realistic outpainted regions when needed ( e.g . , bun hairstyles), preserves identity, expressions, and dynamic hair motion, and faithfully renders common co-occurring objects ( e.g . , headset). The example also highlights strong 3D understanding ( e.g . , flowing confetti). The first example further illustrates that the facial-landmark conditioning is not merely a representation of face location, but instead encodes camera pose and scale disentangled from head motion. Zoom in for higher resolution and details.
| Method | Camera Correctness | ArcFace Similarity | Imaging Quality | Aesthetic Quality | Subject Consistency | Background Consistency | Motion Smoothness | Dynamic Degree |
|---|---|---|---|---|---|---|---|---|
| FaceCam (w/o Synthetic Camera Motion) | 96.00 | 81.19 | 72.03 | 58.02 | 94.27 | 94.91 | 99.29 | 83.00 |
| FaceCam (w/o Multi-shot Stitching) | 86.00 | 76.38 | 70.73 | 55.10 | 94.56 | 95.12 | 99.30 | 80.00 |
| FaceCam (w/o In-the-wild Videos) | 100.00 | 77.73 | 70.71 | 55.73 | 94.52 | 95.16 | 99.23 | 89.00 |
| FaceCam | 97.00 | 83.94 | 73.49 | 59.91 | 94.77 | 94.98 | 99.05 | 96.00 |
| FaceCam (Proxy 3D Head 1) | 97.00 | 84.45 | 73.48 | 59.85 | 94.80 | 94.89 | 99.02 | 95.00 |
| FaceCam (Proxy 3D Head 2) | 97.00 | 84.74 | 73.47 | 59.89 | 94.74 | 94.89 | 99.03 | 94.00 |
Table 3: Ablation study. We conduct ablation studies to quantify the impact of different training data components on the final performance of our model. We also vary the choice of proxy head and show that this selection has negligible effect on the generated results.
Figure 8: Different choices of proxy 3D head . We select two additional proxy 3D heads with different identities and corresponding facial landmark detections, and conduct an ablation study showing that the proxy’s identity and expression do not affect the final generation results.
| ID | Source Camera | Target Camera |
|---|---|---|
| 1 | cam_400944 | cam_401031 |
| 2 | cam_400944 | cam_401410 |
| 3 | cam_400981 | cam_401045 |
| 4 | cam_400981 | cam_401292 |
| 5 | cam_401163 | cam_401031 |
| 6 | cam_401163 | cam_401458 |
Table 4: Source and target camera pairs used in experiment on Ava-256 [ 40 ] .
Figure 9: Ablation study on training data generation. Without Synthetic Camera Motion, the model often produces inaccurate camera trajectories with discontinuous or abrupt changes. Without Multi-shot Stitching, the model cannot learn to change camera angles along a trajectory. With both strategies applied but without in-the-wild videos ( w/o In-the-wild Videos ), the model generates correct camera motion and angle changes, but the lighting remains tied to the training distribution and fails to generalize to real-world illumination, leading to inconsistencies with the source video. Our full model provides accurate camera control and high image quality with lighting and appearance consistent with the source video.
Figure 10: FaceCam performs robustly across diverse, challenging scenarios: it retains head and hair motion from the input video (example 1), captures a wide range of facial expressions (example 2), maintains motion-induced blur from fast body movements (example 3), and plausibly outpaints clothing and background when the generated video contains a smaller face region than the input (example 4).
Figure 11: FaceCam in real-world scenarios. It recaptures a newscaster with detailed facial texture while keeping the studio background consistent (example 1). It further re-synthesizes an e-commerce streamer (example 2) and a singer (example 3) under novel camera angles, accurately maintaining co-occurring objects such as an eyeshadow palette, earrings, headband, necklace, microphone, and glasses, etc . The model even generalizes to cartoon characters (example 4), despite never having seen such content during training.