ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation
- URL: http://arxiv.org/abs/2605.06667v1
- Date: Thu, 07 May 2026 17:59:58 GMT
- Title: ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation
- Abstract summary: ActCam is a zero-shot method for video generation that jointly transfers character motion from a driving video into a new scene.<n>We build on any pretrained image-to-video diffusion model that accepts conditioning in terms of scene depth and character pose.<n>We find that, compared to pose-only control and other pose and camera methods, ActCam improves camera adherence and motion fidelity.
- Score: 34.51506212196978
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: For artistic applications, video generation requires fine-grained control over both performance and cinematography, i.e., the actor's motion and the camera trajectory. We present ActCam, a zero-shot method for video generation that jointly transfers character motion from a driving video into a new scene and enables per-frame control of intrinsic and extrinsic camera parameters. ActCam builds on any pretrained image-to-video diffusion model that accepts conditioning in terms of scene depth and character pose. Given a source video with a moving character and a target camera motion, ActCam generates pose and depth conditions that remain geometrically consistent across frames. We then run a single sampling process with a two-phase conditioning schedule: early denoising steps condition on both pose and sparse depth to enforce scene structure, after which depth is dropped and pose-only guidance refines high-frequency details without over-constraining the generation. We evaluate ActCam on multiple benchmarks spanning diverse character motions and challenging viewpoint changes. We find that, compared to pose-only control and other pose and camera methods, ActCam improves camera adherence and motion fidelity, and is preferred in human evaluations, especially under large viewpoint changes. Our results highlight that careful camera-consistent conditioning and staged guidance can enable strong joint camera and motion control without training. Project page: https://elkhomar.github.io/actcam/.
Related papers
- FaceCam: Portrait Video Camera Control via Scale-Aware Conditioning [45.013802909442184]
We introduce FaceCam, a system that generates video under customizable camera trajectories for monocular human portrait video input.<n> 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.
arXiv Detail & Related papers (2026-03-05T18:59:58Z) - Infinite-Homography as Robust Conditioning for Camera-Controlled Video Generation [49.12018869332346]
InfCam is a camera-controlled video-to-video generation framework with high pose fidelity.<n>The framework integrates two key components: (1) infinite homography warping, which encodes 3D camera rotations directly within the 2D latent space of a video diffusion model.
arXiv Detail & Related papers (2025-12-18T20:03:05Z) - PostCam: Camera-Controllable Novel-View Video Generation with Query-Shared Cross-Attention [13.912161562631722]
PostCam is a framework for novel-view video generation that enables post-capture editing of camera trajectories in dynamic scenes.<n> Experiments on both real-world and synthetic datasets demonstrate that PostCam outperforms state-of-the-art methods by over 20% in camera control precision and view consistency.
arXiv Detail & Related papers (2025-11-21T12:05:46Z) - VividCam: Learning Unconventional Camera Motions from Virtual Synthetic Videos [58.09854638265381]
VividCam is a training paradigm that enables diffusion models to learn complex camera motions from synthetic videos.<n>We demonstrate that our design synthesizes a wide range of precisely controlled and complex camera motions using surprisingly simple synthetic data.
arXiv Detail & Related papers (2025-10-28T19:12:22Z) - ReCamMaster: Camera-Controlled Generative Rendering from A Single Video [72.42376733537925]
ReCamMaster is a camera-controlled generative video re-rendering framework.<n>It reproduces the dynamic scene of an input video at novel camera trajectories.<n>Our method also finds promising applications in video stabilization, super-resolution, and outpainting.
arXiv Detail & Related papers (2025-03-14T17:59:31Z) - AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers [66.29824750770389]
We analyze camera motion from a first principles perspective, uncovering insights that enable precise 3D camera manipulation.<n>We compound these findings to design the Advanced 3D Camera Control (AC3D) architecture.
arXiv Detail & Related papers (2024-11-27T18:49:13Z) - Image Conductor: Precision Control for Interactive Video Synthesis [90.2353794019393]
Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements.
Image Conductor is a method for precise control of camera transitions and object movements to generate video assets from a single image.
arXiv Detail & Related papers (2024-06-21T17:55:05Z) - Training-free Camera Control for Video Generation [15.79168688275606]
We propose a training-free and robust solution to offer camera movement control for off-the-shelf video diffusion models.<n>Our method does not require any supervised finetuning on camera-annotated datasets or self-supervised training via data augmentation.<n>It can be plug-and-play with most pretrained video diffusion models and generate camera-controllable videos with a single image or text prompt as input.
arXiv Detail & Related papers (2024-06-14T15:33:00Z) - CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation [117.16677556874278]
We introduce CamCo, which allows fine-grained Camera pose Control for image-to-video generation.
To enhance 3D consistency in the videos produced, we integrate an epipolar attention module in each attention block.
Our experiments show that CamCo significantly improves 3D consistency and camera control capabilities compared to previous models.
arXiv Detail & Related papers (2024-06-04T17:27:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.