CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation

• URL: http://arxiv.org/abs/2604.09201v1
• Date: Fri, 10 Apr 2026 10:43:18 GMT
• Title: CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation
• Abstract summary: Camera Transformer 1 is a specialized model designed to transfer spatial reasoning knowledge to video generation by accurately estimating camera trajectories.<n>Our framework successfully bridges the gap between spatial reasoning and video synthesis, yielding faithful and high-quality camera-controllable videos.
• Score: 84.82778089348285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Camera-controllable video generation aims to synthesize videos with flexible and physically plausible camera movements. However, existing methods either provide imprecise camera control from text prompts or rely on labor-intensive manual camera trajectory parameters, limiting their use in automated scenarios. To address these issues, we propose a novel Vision-Language-Camera model, termed CT-1 (Camera Transformer 1), a specialized model designed to transfer spatial reasoning knowledge to video generation by accurately estimating camera trajectories. Built upon vision-language modules and a Diffusion Transformer model, CT-1 employs a Wavelet-based Regularization Loss in the frequency domain to effectively learn complex camera trajectory distributions. These trajectories are integrated into a video diffusion model to enable spatially aware camera control that aligns with user intentions. To facilitate the training of CT-1, we design a dedicated data curation pipeline and construct CT-200K, a large-scale dataset containing over 47M frames. Experimental results demonstrate that our framework successfully bridges the gap between spatial reasoning and video synthesis, yielding faithful and high-quality camera-controllable videos and improving camera control accuracy by 25.7% over prior methods.

Related papers

• Geo-Align: Video Generation Alignment via Metric Geometry Reward [53.65904111864641]
  Geo-Align is the first Reinforcement Learning framework specifically designed for camera-controlled video re-rendering.<n>Built upon a pretrained model, we optimize the model through a scale-aware perceptual reward mechanism.<n>Experiments demonstrate that Geo-Align consistently outperforms existing supervised learning baselines in both precise camera controllability and visual fidelity.
  arXiv  Detail & Related papers  (2026-05-22T17:59:43Z)
• Unified Camera Positional Encoding for Controlled Video Generation [48.5789182990001]
  Transformers have emerged as a universal backbone across 3D perception, video generation, and world models for autonomous driving and embodied AI.<n>We introduce Relative Ray, a geometry-consistent representation that unifies complete camera information, including 6-DoF poses, intrinsics, and lens distortions.<n>To facilitate systematic training and evaluation, we construct a large video dataset covering a wide range of camera motions and lens types.
  arXiv  Detail & Related papers  (2025-12-08T07:34:01Z)
• DriveCamSim: Generalizable Camera Simulation via Explicit Camera Modeling for Autonomous Driving [9.882070476776274]
  We present a generalizable camera simulation framework DriveCamSim.<n>Our core innovation lies in the proposed Explicit Camera Modeling mechanism.<n>For controllable generation, we identify the issue of information loss inherent in existing conditional encoding and injection pipelines.
  arXiv  Detail & Related papers  (2025-05-26T08:50:15Z)
• CPA: Camera-pose-awareness Diffusion Transformer for Video Generation [15.512186399114999]
  CPA is a text-to-video generation approach that integrates the textual, visual, and spatial conditions.<n>Our method outperforms LDM-based methods for long video generation while achieving optimal performance in trajectory consistency and object consistency.
  arXiv  Detail & Related papers  (2024-12-02T12:10:00Z)
• 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)
• VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control [74.5434726968562]
  We show how to tame transformers video for 3D camera control using a ControlNet-like conditioning mechanism.<n>Our work is the first to enable camera control for transformer-based video diffusion models.
  arXiv  Detail & Related papers  (2024-07-17T17:59:05Z)
• CameraCtrl: Enabling Camera Control for Text-to-Video Generation [86.36135895375425]
  Controllability plays a crucial role in video generation, as it allows users to create and edit content more precisely.<n>Existing models, however, lack control of camera pose that serves as a cinematic language to express deeper narrative nuances.<n>We introduce CameraCtrl, enabling accurate camera pose control for video diffusion models.
  arXiv  Detail & Related papers  (2024-04-02T16:52:41Z)

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.