Automatic Camera Trajectory Control with Enhanced Immersion for Virtual Cinematography

• URL: http://arxiv.org/abs/2303.17041v3
• Date: Tue, 21 May 2024 22:26:39 GMT
• Title: Automatic Camera Trajectory Control with Enhanced Immersion for Virtual Cinematography
• Authors: Xinyi Wu, Haohong Wang, Aggelos K. Katsaggelos,
• Abstract summary: Real-world cinematographic rules show that directors can create immersion by comprehensively synchronizing the camera with the actor. Inspired by this strategy, we propose a deep camera control framework that enables actor-camera synchronization in three aspects. Our proposed method yields immersive cinematic videos of high quality, both quantitatively and qualitatively.
• Score: 23.070207691087827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: User-generated cinematic creations are gaining popularity as our daily entertainment, yet it is a challenge to master cinematography for producing immersive contents. Many existing automatic methods focus on roughly controlling predefined shot types or movement patterns, which struggle to engage viewers with the circumstances of the actor. Real-world cinematographic rules show that directors can create immersion by comprehensively synchronizing the camera with the actor. Inspired by this strategy, we propose a deep camera control framework that enables actor-camera synchronization in three aspects, considering frame aesthetics, spatial action, and emotional status in the 3D virtual stage. Following rule-of-thirds, our framework first modifies the initial camera placement to position the actor aesthetically. This adjustment is facilitated by a self-supervised adjustor that analyzes frame composition via camera projection. We then design a GAN model that can adversarially synthesize fine-grained camera movement based on the physical action and psychological state of the actor, using an encoder-decoder generator to map kinematics and emotional variables into camera trajectories. Moreover, we incorporate a regularizer to align the generated stylistic variances with specific emotional categories and intensities. The experimental results show that our proposed method yields immersive cinematic videos of high quality, both quantitatively and qualitatively. Live examples can be found in the supplementary video.

Related papers

• ChatCam: Empowering Camera Control through Conversational AI [67.31920821192323]
  ChatCam is a system that navigates camera movements through conversations with users. To achieve this, we propose CineGPT, a GPT-based autoregressive model for text-conditioned camera trajectory generation. We also develop an Anchor Determinator to ensure precise camera trajectory placement.
  arXiv  Detail & Related papers  (2024-09-25T20:13:41Z)
• DreamCinema: Cinematic Transfer with Free Camera and 3D Character [11.979669977372707]
  We propose DreamCinema, a novel cinematic transfer framework that pioneers generative AI into the film production paradigm. Specifically, we first extract cinematic elements (i.e., human and camera pose) and optimize the camera trajectory. Then, we apply a character generator to efficiently create 3D high-quality characters with a human structure prior.
  arXiv  Detail & Related papers  (2024-08-22T17:59:44Z)
• MotionBooth: Motion-Aware Customized Text-to-Video Generation [44.41894050494623]
  MotionBooth is a framework designed for animating customized subjects with precise control over both object and camera movements. We efficiently fine-tune a text-to-video model to capture the object's shape and attributes accurately. Our approach presents subject region loss and video preservation loss to enhance the subject's learning performance.
  arXiv  Detail & Related papers  (2024-06-25T17:42:25Z)
• 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)
• Cinematic Behavior Transfer via NeRF-based Differentiable Filming [63.1622492808519]
  Existing SLAM methods face limitations in dynamic scenes and human pose estimation often focuses on 2D projections. We first introduce a reverse filming behavior estimation technique. We then introduce a cinematic transfer pipeline that is able to transfer various shot types to a new 2D video or a 3D virtual environment.
  arXiv  Detail & Related papers  (2023-11-29T15:56:58Z)
• Total-Recon: Deformable Scene Reconstruction for Embodied View Synthesis [76.72505510632904]
  We present Total-Recon, the first method to reconstruct deformable scenes from long monocular RGBD videos. Our method hierarchically decomposes the scene into the background and objects, whose motion is decomposed into root-body motion and local articulations.
  arXiv  Detail & Related papers  (2023-04-24T17:59:52Z)
• Decoupling Dynamic Monocular Videos for Dynamic View Synthesis [50.93409250217699]
  We tackle the challenge of dynamic view synthesis from dynamic monocular videos in an unsupervised fashion. Specifically, we decouple the motion of the dynamic objects into object motion and camera motion, respectively regularized by proposed unsupervised surface consistency and patch-based multi-view constraints.
  arXiv  Detail & Related papers  (2023-04-04T11:25:44Z)
• 3D Moments from Near-Duplicate Photos [67.15199743223332]
  3D Moments is a new computational photography effect. We produce a video that smoothly interpolates the scene motion from the first photo to the second. Our system produces photorealistic space-time videos with motion parallax and scene dynamics.
  arXiv  Detail & Related papers  (2022-05-12T17:56:18Z)
• Batteries, camera, action! Learning a semantic control space for expressive robot cinematography [15.895161373307378]
  We develop a data-driven framework that enables editing of complex camera positioning parameters in a semantic space. First, we generate a database of video clips with a diverse range of shots in a photo-realistic simulator. We use hundreds of participants in a crowd-sourcing framework to obtain scores for a set of semantic descriptors for each clip.
  arXiv  Detail & Related papers  (2020-11-19T21:56:53Z)

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.