ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation

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 (i) transfers character motion from a driving video into a new scene and (ii) 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+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/.