RealCam: Real-Time Novel-View Video Generation with Interactive Camera Control

• URL: http://arxiv.org/abs/2605.06051v1
• Date: Thu, 07 May 2026 11:36:29 GMT
• Title: RealCam: Real-Time Novel-View Video Generation with Interactive Camera Control
• Abstract summary: Real-time camera-controlled video-to-video (V2V) generation has immense potential for interactive filmmaking and live broadcasting.<n>Existing implicit synthesis methods rely on non-causal, full-sequence processing and rigid prefix-style temporal concatenation.<n>We introduce textttRealCam, a novel autoregressive framework for interactive, real-time camera-controlled V2V generation.
• Score: 30.825728569025525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Camera-controlled video-to-video (V2V) generation enables dynamic viewpoint synthesis from monocular footage, holding immense potential for interactive filmmaking and live broadcasting. However, existing implicit synthesis methods fundamentally rely on non-causal, full-sequence processing and rigid prefix-style temporal concatenation. This architectural paradigm mandates bidirectional attention, resulting in prohibitive computational latency, quadratic complexity scaling, and inherent incompatibility with real-time streaming or variable-length inputs. To overcome these limitations, we introduce \texttt{RealCam}, a novel autoregressive framework for interactive, real-time camera-controlled V2V generation. We first design a high-fidelity teacher model grounded in a \textbf{Cross-frame In-context Learning} paradigm. By interleaving source and target frames into synchronized contextual pairs, our design inherently enables length-agnostic generalization and naturally facilitates causal adaptation, breaking the rigid prefix bottleneck. We then distill this teacher into a few-step causal student via Self-Forcing with Distribution Matching Distillation, enabling efficient, on-the-fly streaming synthesis. Furthermore, to mitigate severe loop inconsistency in closed-loop trajectories, we propose \textbf{Loop-Closed Data Augmentation (LoopAug)}, a novel paradigm that synthesizes globally consistent loop sequences from existing multiview datasets. Extensive experiments demonstrate that \texttt{RealCam} achieves state-of-the-art visual fidelity and temporal consistency while enabling truly interactive camera control with orders-of-magnitude faster inference than existing paradigms. Our project page is at https://xyc-fly.github.io/RealCam/.

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