Morphing is a long-standing problem in vision and computer graphics, requir- ing a time-dependent warping for feature alignment and a blending for smooth interpolation. Recently, multilayer perceptrons (MLPs) have been explored as implicit neural representations (INRs) for modeling such deformations, due to their meshlessness and differentiability; however, extracting coherent and accurate morphings from standard MLPs typically relies on costly regularizations, which often lead to unstable training and prevent effective feature alignment. To overcome these limitations, we propose FLOWING (FLOW morphING), a framework that recasts warping as the construction of a differential vector flow, naturally ensuring continuity, invertibility, and temporal coherence by encoding structural flow prop- erties directly into the network architectures. This flow-centric approach yields principled and stable transformations, enabling accurate and structure-preserving morphing of both 2D images and 3D shapes. Extensive experiments across a range of applications—including face and image morphing, as well as Gaussian Splatting morphing—show that FLOWING achieves state-of-the-art morphing quality with faster convergence. Code and pretrained models are available in https://schardong.github.io/flowing.

Volumetric radiance fields have been popular in reconstructing small-scale 3D scenes from multi-view images. With additional constraints such as person correspondences, reconstructing a large 3D scene with multiple persons becomes possible. However, existing methods fail for sparse input views or when person correspondences are unavailable. In such cases, the conventional depth image supervision may be insufficient because it only captures the relative position of each person with respect to the camera center. In this paper, we investigate an alternative approach by supervising the optimization framework with a dense pose prior that represents correspondences between the SMPL model and the input images. The core ideas of our approach consist in exploiting dense pose priors estimated from the input images to perform person segmentation and incorporating such priors into the learning of the radiance field. Our proposed dense pose supervision is view-independent, significantly speeding up computational time and improving 3D reconstruction accuracy , with less floaters and noise. We confirm the advantages of our proposed method with extensive evaluation in a subset of the publicly available CMU Panoptic dataset. When training with only five input views, our proposed method achieves an average improvement of 6.1% in PSNR , 3.5% in SSIM , 17.2% in LPIPS vgg , 19.3% in LPIPS alex , and 39.4% in training time.

This paper presents a method for localizing primitive shapes in a dense point cloud computed by the RGB-D SLAM system. To stably generate a shape map containing only primitive shapes, the primitive shape is incrementally modeled by fusing the shapes estimated at previous frames in the SLAM, so that an accurate shape can be finally generated. Specifically, the history of the fusing process is used to avoid the influence of error accumulation in the SLAM. The point cloud of the shape is then updated by fusing the points in all the previous frames into a single point cloud. In the experimental results, we show that metric primitive modeling in texture-less and unprepared environments can be achieved online.

Finding high-level semantic information from a point cloud is a challenging task, and it can be used in various applications. For instance, it is useful to compactly represent the scene structure and efficiently understand the scene context. This task is even more challenging when using a hand-held monocular visual SLAM system that outputs a noisy sparse point cloud. In order to tackle this issue, we propose an incremental primitive modeling method using both geometric and statistical analyses for such point cloud. The main idea is to select only reliably-modeled shapes by analyzing the geometric relationship between the point cloud and the estimated shapes. Besides that, a statistical evaluation is incorporated to filter wrongly-detected primitives in a noisy point cloud. As a result of this processing, our approach largely improved precision when compared with state of the art methods. We also show the impact of segmenting and representing a scene using primitives instead of a point cloud.

This paper introduces a toolkit with camera calibration, monocular visual Simultaneous Localization and Mapping (vSLAM) and registration with a calibration marker. With the toolkit, users can perform the whole procedure of the ISMAR on-site tracking competition in 2015. Since the source code is designed to be well-structured and highly-readable, users can easily install and modify the toolkit. By providing the toolkit, we encourage beginners to learn tracking techniques and to participate in the competition.

According to Gartner's 2013 Hype Cycle for Emerging Technologies, Augmented Reality will reach its Plateu of Productivity before Wearable User Interfaces. In this work, device and user-perspective rendering are compared regarding their applicability to AR-based solutions for Google Glass. The conducted experiment measured and evaluated the advantages and drawbacks on each method and also got positive and negative feedbacks given by users. The tests showed that users preferred the device-perspective approach.

This demonstration allows visitors to use AR applications for monocular optical see-through head-mounted displays with two forms of visualization. One is the device-perspective approach, in which the user sees the virtual content registered with the camera image at the display. The other is the user-perspective method, in which the display is used as a de facto optical see-through device and the virtual content is registered with the real world.

This demo presents an AR application that helps the user to solve a jigsaw puzzle that consists of non-textured pieces with a discriminative shape. The pieces are detected, their poses are estimated and the ones that are correctly assembled are highlighted. In order to detect the pieces, the Depth-Assisted Rectification of Contours (DARC) method is used, which performs detection and pose estimation of texture-less planar objects using an RGB-D camera.

This paper presents a method named Depth-Assisted Rectification of Contours (DARC) for detection and pose estimation of texture-less planar objects using RGB-D cameras. It consists in matching contours extracted from the current image to previously acquired template contours. In order to achieve invariance to rotation, scale and perspective distortions, a rectified representation of the contours is obtained using the available depth information. DARC requires only a single RGB-D image of the planar objects in order to estimate their pose, opposed to some existing approaches that need to capture a number of views of the target object. It also does not require to generate warped versions of the templates, which is commonly needed by existing object detection techniques. It is shown that the DARC method runs in real-time and its detection and pose estimation quality are suitable for augmented reality applications.

Augmented Reality applications overlap virtual objects over a real scene, taking into account the context, in order to add information to the end user. Nowadays, more advanced applications also make use of Diminished Reality that removes real objects from a scene. This paper describes an approach that combines Augmented Reality and Diminished Reality techniques to modify real objects present in applications. The proposed approach removes an object and replaces it with its purposely-modified replica. The solution uses dynamic texture techniques and Inpaint to enhance the visual response of the modification performed. The results are promising considering both realism of the modified real object and performance of the application.

Augmented Reality applications overlap virtual objects over a real scene, taking into account context, in order to add information to the end user. Nowadays, more advanced applications also make use of Diminished Reality that removes real objects from a scene. This paper describes an approach that combines Augmented Reality and Diminished Reality techniques to modify real objects present in applications. The proposed approach removes an object and replaces it with its purposely-modified replica. The solution uses dynamic texture techniques and Inpaint to enhance the visual response of the modification performed. The results are promising considering both realism of the modified real object and performance of the application.

This paper presents a solution for the photorealistic rendering of synthetic objects into dynamic real scenes, in Augmented Reality applications. In order to achieve this goal, an Image Based Lighting approach is used, where environment maps with different levels of glossiness are generated for each virtual object in the scene at every frame. Due to this, illumination effects, such as color bleeding and specular reflections, can be simulated for virtual objects in a consistent way. A unifying sampling method for the spherical harmonics transformation pass is also used. It is independent of map format and does not need to apply different weights for each sample. The developed technique is combined with an extended version of Lafortune Spatial BRDF, featuring Fresnel effect and an innovative tangent rotation parameterization. The solution is evaluated in various Augmented Reality case studies, where other features like shadowing and lens effects are also exploited.

This paper presents a solution for the photorealistic rendering of synthetic objects into dynamic real scenes, in Augmented Reality applications. In order to achieve this goal, an Image Based Lighting approach is proposed, where environment maps with different levels of glossiness are generated for each virtual object in the scene at every frame. Due to this, illumination effects, such as color bleeding and specular reflections, can be simulated for virtual objects in a consistent way, even under the presence of scene changes. A unifying sampling method for the spherical harmonics transformation pass is also used. It is independent of map format and does not need to apply different weights for each sample. The developed technique is combined with an extended version of Lafortune Spatial BRDF, featuring Fresnel effect and an innovative tangent rotation parameterization. The solution is evaluated in various Augmented Reality case studies, where other features like shadowing and lens effects are also exploited.

This paper presents a markerless tracking technique targeted to the Windows Mobile Pocket PC platform. The primary aim of this work is to allow the development of standalone augmented reality applications for handheld devices based on natural feature tracking of fully 3-Dimensional objects. In order to achieve this goal, a model-based tracking approach that relies on edge information was adopted. Since it does not require high processing power, it is suitable for constrained devices such as handhelds. The OpenGL ES graphics library was used to detect the visible edges in a given frame, taking advantage of graphics hardware acceleration when available. In addition, a subset of two computer vision libraries was ported to the Pocket PC platform in order to provide some required algorithms to the markerless mobile solution. They were also adapted to use fixed-point math, with the purpose of improving the overall performance of the routines. The port of these libraries opens up the possibility of having other computer-vision tasks being executed on mobile platforms. An augmented reality application was created using the implemented technique and evaluations were done regarding tracking performance, accuracy and robustness. In most of the tests, the frame rates obtained are suitable for handheld augmented reality and a reasonable estimation of the object pose was provided.