Character animation is a critical component of games and interactive applications. Recent data-driven methods rely on motion capture to generate high-quality real-time locomotion models for movement such as walking or running. However, these methods do not easily accommodate more subtle movement such as side steps or turning, necessary for the character to reach a precise position and orientation. These stepping movements are often needed when the character must interact with the environment, such as positioning in front of a chair so a sit-down animation can be played appropriately. This work addresses the problem of generating such motions in the low-data regime. Our goal is to create an expressive locomotion system able to reach a precise goal in the vicinity of a character. Our method takes as input raw motion capture data, and automatically converts it to a sequence database. At runtime, our system selects the most appropriate sequence and adapts it to very precisely reach the target. Furthermore, our system has a negligible performance impact, which makes it suitable for use in AR applications and video games.

This work addresses the development of a character animation editing method that accommodates animation changes while preserving the animator’s original artistic intent. Our goal is to give the artist control over the automatic editing of animations by extending them with artist-defined metadata. We propose a metadata representation that describes which aspects of an animation can be varied. To make the authoring process easier, we have developed an interface for specifying the metadata. Our method extracts a collection of trajectories of both effectors and objects for the animation. We approximate and parameterize the trajectories with a series of cubic Bézier curves. Then, we generate a set of high-level parameters for editing which are related to trajectory deformations. The only possible deformations are those that preserve the fine structure of the original motion. From the trajectories, we use inverse kinematics to generate a new animation that conforms to the user’s edits while preserving the overall character of the original.

A narrative relies on the imperfect knowledge of the user to create interactions between the characters that are ultimately used as a plot device to drive the narrative. This motivates our exploration of ways to encode this information, provides means for a user to both query and influence the knowledge, and guides the user based on a model of their experience. We developed PICA: a proactive intelligent conversational agent for interactive narratives that can guide users through such experiences. The underlying knowledge base is designed using a sub-symbolic architecture, which encodes belief models for multiple users and autonomous agents in addition to the actual story knowledge. We also developed a discourse module using Behavior Trees to intuitively design the proactive and reactive capabilities of PICA. We compare our approach to neural networks and symbolic knowledge bases and demonstrate its functionality.

A pervasive artifact that occurs when visualizing 3D content is the so-called "cardboarding" effect, where objects appear flat due to depth compression, with relatively little research conducted to perceptually quantify its effects. Our aim is to shed light on the subjective preferences and practical perceptual limits of stereo vision with respect to cardboarding. We present three experiments that explore the consequences of displaying simple scenes with reduced depths using both subjective ratings and adjustments and objective sensitivity metrics. Our results suggest that compressing depth to 80% or above is likely to be acceptable, whereas sensitivity to the cardboarding artifact below 30% is very high. These values could be used in practice as guidelines for commonplace depth mapping operations in 3D production pipelines.

The decoupling of eye vergence and accommodation (V/A) has been found to negatively impact depth interpretation, visual comfort and fatigue. In this paper, we explore a hypothesis that placement of visual cues within a scene can assist a viewer in the process of maintaining the V/A decoupling. This effect is demonstrated through the use of a continuous depth plane that connects spatially distinct scene elements. Our experimental design enables us to make the following three contributions: (1) We show that a continuous depth element can improve the time it takes to transition visual attention in depth. (2) We observe that the subjective assessment of fatigue emerges before we detect a quantitative decline in performance. (3) We aim to motivate that stereoscopic 3D content creators may learn scene composition, framing and montage from visual psychophysics.

REVERIE (REal and Virtual Engagement in Realistic Immersive Environments [1]) targets novel research to address the demanding challenges involved with developing state-of-the-art technologies for online human interaction. The REVERIE framework enables users to meet, socialise and share experiences online by integrating cutting-edge technologies for 3D data acquisition and processing, networking, autonomy and real-time rendering. In this paper, we describe the innovative research that is showcased through the REVERIE integrated framework through richly defined use-cases which demonstrate the validity and potential for natural interaction in a virtual immersive and safe environment. Previews of the REVERIE demo and its key research components can be viewed at www.youtube.com/user/REVERIEFP7.

Abstract We present a novel stereo‐to‐multiview video conversion method for glasses‐free multiview displays. Different from previous stereo‐to‐multiview approaches, our mapping algorithm utilizes the limited depth range of autostereoscopic displays optimally and strives to preserve the scene's artistic composition and perceived depth even under strong depth compression. We first present an investigation of how perceived image quality relates to spatial frequency and disparity. The outcome of this study is utilized in a two‐step mapping algorithm, where we (i) compress the scene depth using a non‐linear global function to the depth range of an autostereoscopic display and (ii) enhance the depth gradients of salient objects to restore the perceived depth and salient scene structure. Finally, an adapted image domain warping algorithm is proposed to generate the multiview output, which enables overall disparity range extension.

This paper addresses the problem of remapping the disparity range of stereoscopic images and video. Such operations are highly important for a variety of issues arising from the production, live broadcast, and consumption of 3D content. Our work is motivated by the observation that the displayed depth and the resulting 3D viewing experience are dictated by a complex combination of perceptual, technological, and artistic constraints. We first discuss the most important perceptual aspects of stereo vision and their implications for stereoscopic content creation. We then formalize these insights into a set of basic disparity mapping operators. These operators enable us to control and retarget the depth of a stereoscopic scene in a nonlinear and locally adaptive fashion. To implement our operators, we propose a new strategy based on stereoscopic warping of the input video streams. From a sparse set of stereo correspondences, our algorithm computes disparity and image-based saliency estimates, and uses them to compute a deformation of the input views so as to meet the target disparities. Our approach represents a practical solution for actual stereo production and display that does not require camera calibration, accurate dense depth maps, occlusion handling, or inpainting. We demonstrate the performance and versatility of our method using examples from live action post-production, 3D display size adaptation, and live broadcast. An additional user study and ground truth comparison further provide evidence for the quality and practical relevance of the presented work.