Creating high-fidelity 3D meshes with arbitrary topology, including open surfaces and complex interiors, remains a significant challenge. Existing implicit field methods often require costly and detail-degrading watertight conversion, while other approaches struggle with high resolutions. This paper introduces SparseFlex, a novel sparse-structured isosurface representation that enables differentiable mesh reconstruction at resolutions up to $1024^3$ directly from rendering losses. SparseFlex combines the accuracy of Flexicubes with a sparse voxel structure, focusing computation on surface-adjacent regions and efficiently handling open surfaces. Crucially, we introduce a frustum-aware sectional voxel training strategy that activates only relevant voxels during rendering, dramatically reducing memory consumption and enabling high-resolution training. This also allows, for the first time, the reconstruction of mesh interiors using only rendering supervision. Building upon this, we demonstrate a complete shape modeling pipeline by training a variational autoencoder (VAE) and a rectified flow transformer for high-quality 3D shape generation. Our experiments show state-of-the-art reconstruction accuracy, with a ~82% reduction in Chamfer Distance and a ~88% increase in F-score compared to previous methods, and demonstrate the generation of high-resolution, detailed 3D shapes with arbitrary topology. By enabling high-resolution, differentiable mesh reconstruction and generation with rendering losses, SparseFlex significantly advances the state-of-the-art in 3D shape representation and modeling.

Existing vector font generation approaches either struggle to preserve high-frequency corner details of the glyph or produce vector shapes that have redundant segments, which hinders their applications in practical scenarios. In this paper, we propose to learn vector fonts from pixelated font images utilizing a joint neural representation that consists of a signed distance field (SDF) and a probabilistic corner field (CF) to capture shape corner details. To achieve smooth shape interpolation on the learned shape manifold, we establish connections between the two fields for better alignment. We further design a vectorization process to extract high-quality and compact vector fonts from our joint neural representation. Experiments demonstrate that our method can generate more visually appealing vector fonts with a higher level of compactness compared to existing alternatives.

Redirected walking (RDW) allows users to explore virtual environments in limited physical spaces by imperceptibly steering them away from obstacles and space boundaries. However, even with those techniques, the risk of collision cannot always be avoided. For such situations, resetting techniques have been proposed to provide an immediate adjustment of the physical walking direction of a user. Existing resetting techniques are either applied in-place, where the user changes orientation but stays in the same position or out-of-place methods where the user is guided to move from the current position to a safe location all while freezing the movement in the virtual world. While out-of-place methods have the potential to provide more freedom to user movements after resetting, current out-of-place methods do not provide enough guidance for the users to move to optimal locations. In this work, we propose a novel out-of-place resetting strategy that guides users to optimal physical locations with the most potential for free movement and a smaller amount of resetting required for their further movements. For this purpose, we calculate a heat map of the walking area according to the average walking distance using a simulation of the currently used RDW algorithm. Based on this heat map, we identify the most suitable position for a one-step reset within a predefined searching range and use this one as the reset point. Our results show that our method increases the average moving distance within one cycle of resetting. Furthermore, our resetting method can be applied to any physical area with obstacles. That means that RDW methods that were not suitable for such environments (e.g., Steer to Center) combined with our resetting can also be extended to such complex walking areas. In addition, we present a user interface to provide a similar visual experience between these methods, using a two-arrows indicator to help users adjust their position and direction.

Redirected Walking (RDW) algorithms aim to impose several types of gains on users immersed in Virtual Reality and distort their walking paths in the real world, thus enabling them to explore a larger space. Since collision with physical boundaries is inevitable, a reset strategy needs to be provided to allow users to reset when they hit the boundary. However, most reset strategies are based on simple heuristics by choosing a seemingly suitable solution, which may not perform well in practice. In this article, we propose a novel optimization-based reset algorithm adaptive to different RDW algorithms. Inspired by the approach of finite element analysis, our algorithm splits the boundary of the physical world by a set of endpoints. Each endpoint is assigned a reset vector to represent the optimized reset direction when hitting the boundary. The reset vectors on the edge will be determined by the interpolation between two neighbouring endpoints. We conduct simulation-based experiments for three RDW algorithms with commonly used reset algorithms to compare with. The results demonstrate that the proposed algorithm significantly reduces the number of resets.

Redirected walking (RDW) aims to reduce the collisions in the physical space for VR applications. However, most of the previous RDW methods do not consider future possibilities of collisions after imperceptibly redirecting users. In this paper, we combine the subtle RDW methods and reset strategy in our method design and propose a novel solution for RDW that can make better use of physical space and trigger fewer resets. The key idea of our method is to discretize the representation of possible user positions and orientations by a series of standard poses and rate them based on the possibilities of hitting obstacles of their reachable poses. A transfer path algorithm is proposed to measure the accessibility among standard poses and is used to support the calculation of the scores of standard poses. Using our method, the user can be redirected imperceptibly to the optimal pose with the best score among all the reachable poses from the user’s current pose during walking. Experiments demonstrate that our method outperforms state-of-the-art methods in various environment sizes and obstacle layouts.

Most state-of-the-art dialogue state tracking (DST) methods infer the dialogue state based on ground-truth transcriptions of utterances. In real-world situations, utterances are transcribed by automatic speech recognition (ASR) systems, which output the n-best candidate transcriptions (hypotheses). In certain noisy environments, the best transcription is often imperfect, severely influencing DST accuracy and possibly causing the dialogue system to stall or loop. The missed or misrecognized words can often be found in the runner-up candidate transcriptions from 2 to n, which could be used to improve accuracy of DST. However, looking beyond the top-ranked ASR results poses a dilemma: going too far may introduce noise, while not going far enough may not uncover any useful information. In this paper, we propose a novel approach to automatically determine the optimal time to stop reexamining runner-up ASR transcriptions based on deep reinforcement learning. Our method outperforms the baseline system, which uses only the top-1 ASR result, by 3.1%. Then, we select the dialogue rounds with the top-10 largest word error rate (WER), our method can improve DST accuracy by 15.4%, which is five times the overall improvement rate (3.1%). This improvement was expected because our proposed method is able to select informative ASR results at any rank.