InfCam: Infinite-Homography as Robust Conditioning for Camera-Controlled
Video Generation

Min-Jung Kim*, Jeongho Kim*, Hoiyeong Jin, Junha Hyung, Jaegul Choo
*Indicates Equal Contribution
KAIST GSAI
Paper Code

Overview

We present InfCam, a depth-free, camera-controlled video generation framework with high pose fidelity. InfCam introduces infinite homography warping, which encodes 3D camera rotations directly in the 2D latent space, allowing the model to focus on learning the residual parallax for accurate camera control. Combined with a data augmentation pipeline for diverse trajectories and focal lengths, InfCam outperforms baselines in both camera-pose accuracy and visual fidelity.

Motivation

Motivation

In the reprojection-based approach, inaccuracies in the depth estimation lead to unreliable conditioning, consequently introducing artifacts in the generated frame. In contrast, based on the fact that reprojection can be expressed by the following equation,

Equation: Reprojection formula
our infinite homography-based approach conditions on noise-free frame warped by infinite homography. This forces the model to concentrate on learning the parallax relative to the plane at infinity. This parallax is spatially constrained to the region between the epipole e' and the warped point x_inf, as visualized by the yellow segment on the epipolar line l’. This constraint reduces the search space, enabling the model to achieve higher camera pose fidelity.

Qualitative Results

Qualitative Comparison on Synthetic Data

Per-Method Comparison


Per-Method Comparison
(including ReCamMaster w/ interp)

Quantitative Comparison

AugMCV result
AugMCV dataset
WebVid result
WebVid dataset

AugMCV dataset. We evaluate our method under two scenarios: (1) source and target videos with identical camera intrinsics, and (2) source and target videos with different camera intrinsics. Across both settings and all metrics, our approach consistently outperforms the baselines, producing videos that are clearly closer to the ground truth.
WebVid dataset. We further validate our method on the WebVid dataset, where it again consistently outperforms baseline approaches in terms of both camera pose accuracy and visual fidelity, with particularly pronounced gains in camera pose accuracy.

BibTeX

@article{kim2025infinite,
  title={Infinite-Homography as Robust Conditioning for Camera-Controlled Video Generation},
  author={Kim, Min-Jung and Kim, Jeongho and Jin, Hoiyeong and Hyung, Junha and Choo, Jaegul},
  journal={arXiv preprint arXiv:2512.17040},
  year={2025}
}
}