Narrated 360 videos are typically provided in many touring scenarios to mimic real-world experience. However, previous work has shown that smart assistance (i.e., providing visual guidance) can significantly help users to follow the Normal Field of View (NFoV) corresponding to the narrative. In this project, we aim at automatically grounding the NFoVs of a 360 video given subtitles of the narrative (referred to as "NFoV-grounding"). We propose a novel Visual Grounding Model (VGM) to implicitly and efficiently predict the NFoVs given the video content and subtitles. Specifically, at each frame, we efficiently encode the panorama into feature map of candidate NFoVs using a Convolutional Neural Network (CNN) and the subtitles to the same hidden space using an RNN with Gated Recurrent Units (GRU). Then, we apply soft-attention on candidate NFoVs to trigger sentence decoder aiming to minimize the reconstruct loss between the generated and given sentence. Finally, we obtain the NFoV as the candidate NFoV with the maximum attention without any human supervision. To train VGM more robustly, we also generate a reverse sentence conditioning on one minus the soft-attention such that the attention focuses on candidate NFoVs less relevant to the given sentence. The negative log reconstruction loss of the reverse sentence (referred to as "irrelevant loss") is jointly minimized to encourage the reverse sentence to be different from the given sentence. To evaluate our method, we collect the first narrated 360 videos dataset and achieve state-of-the-art NFoV-grounding performance.
A listing of virtual cinematography research @cite_39 @cite_38 @cite_35 focused on controlling a camera view in virtual gaming environments and did not take the problem of perception difficulties into consideration. Some works @cite_14 @cite_31 @cite_26 relaxed such perception assumption. They manipulated virtual cameras within a static video with wide field-of-view of a teleconference, a basketball court, or a classroom, where objects of interest could be easily extracted.
@cite_38 @cite_35 @cite_14 @cite_26 @cite_39 @cite_31
2105081120 2295415742 2150417956 2436270489 1802459088 2062400178
Machinima is a low-cost alternative to full production filmmaking. However, creating quality cinematic visualizations with existing machinima techniques still requires a high degree of talent and effort. We introduce a lightweight artificial intelligence system, Cambot, that can be used to assist in machinima production. Cambot takes a script as input and produces a cinematic visualization. Unlike other virtual cinematography systems, Cambot favors an offline algorithm coupled with an extensible library of specific modular and reusable facets of cinematic knowledge. One of the advantages of this approach to virtual cinematography is a tight coordination between the positions and movements of the camera and the actors. We present a novel method for creating automatized gameplay dramatization of multiplayer video games. The dramatization serves as a visual form of guidance through dynamic 3D scenes with multiple foci, typical for such games. Our goal is to convey interesting aspects of the gameplay by animated sequences creating a summary of events which occurred during the game. Our technique is based on processing many cameras, which we refer to as a flock of cameras, and events captured during the gameplay, which we organize into a so-called event graph. Each camera has a lifespan with a certain time interval and its parameters such as position or look-up vector are changing over time. Additionally, during its lifespan each camera is assigned an importance function, which is dependent on the significance of the structures that are being captured by the camera. The images captured by the cameras are composed into a single continuous video using a set of operators based on cinematographic effects. The sequence of operators is selected by traversing the event graph and looking for specific patterns corresponding to the respective operators. In this way, a large number of cameras can be processed to generate an informative visual story presenting the gameplay. Our compositing approach supports insets of camera views to account for several important cameras simultaneously. Additionally, we create seamless transitions between individual selected camera views in order to preserve temporal continuity, which helps the user to follow the virtual story of the gameplay. We present a system for automatically extracting the region of interest (ROI) and controlling virtual cameras' control based on panoramic video. It targets applications such as classroom lectures and video conferencing. For capturing panoramic video, we use the FlyCam system that produces high resolution, wide-angle video by stitching video images from multiple stationary cameras. To generate conventional video, a region of interest can be cropped from the panoramic video. We propose methods for ROI detection, tracking, and virtual camera control that work in both the uncompressed and compressed domains. The ROI is located from motion and color information in the uncompressed domain and macroblock information in the compressed domain, and tracked using a Kalman filter. This results in virtual camera control that simulates human controlled video recording. The system has no physical camera motion and the virtual camera parameters are readily available for video indexing. We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e.g., players in a basketball game). The conventional approach for training predictors does not directly consider temporal consistency, and often produces undesirable jitter. Although post-hoc smoothing (e.g., via a Kalman filter) can mitigate this issue to some degree, it is not ideal due to overly stringent modeling assumptions (e.g., Gaussian noise). We propose a recurrent decision tree framework that can directly incorporate temporal consistency into a data-driven predictor, as well as a learning algorithm that can efficiently learn such temporally smooth models. Our approach does not require any post-processing, making online smooth predictions much easier to generate when the noise model is unknown. We apply our approach to sports broadcasting: given noisy player detections, we learn where the camera should look based on human demonstrations. Our experiments exhibit significant improvements over conventional baselines and showcase the practicality of our approach. Animations generated by interactive 3D computer graphics applications are typically portrayed either from a particular character's point of view or from a small set of strategically-placed viewpoints. By ignoring camera placement, such applications fail to realize important storytelling capabilities that have been explored by cinematographers for many years. In this paper, we describe several of the principles of cinematography and show how they can be formalized into a declarative language, called the Declarative Camera Control Language (OCCL). We describe the application of OCCL within the context of a simple interactive video game and argue that OCCL represents cinematic knowledge at the same level of abstraction as expert directors by encoding 16 idioms from a film textbook. These idioms produce compelling animations, as demonstrated on the accompanying videotape. Filming team sports is challenging because there are many points of interest which are constantly changing. Unlike previous automatic broadcasting solutions, we propose a data-driven approach for determining where a robotic pan-tilt-zoom (PTZ) camera should look. Without using any pre-defined heuristics, we learn the relationship between player locations and corresponding camera configurations by crafting features which can be derived from noisy player tracking data, and employ a new calibration algorithm to estimate the pan-tilt-zoom configuration of a human operated broadcast camera at each video frame. Using this data, we train a regress or to predict the appropriate pan angle for new noisy input tracking data. We demonstrate our system on a high school basketball game. Our experiments show how our data-driven planning approach achieves superior performance to a state-of-the-art algorithm and does indeed mimic a human operator.