The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE's reconstruction capability sets a firm upper bound on generation quality. A fundamental challenge limiting existing VAEs is the representation mismatch between ground-truth meshes and network predictions: GT meshes have arbitrary, variable topology, while VAEs typically predict fixed-structure implicit fields (, SDF on regular grids). This inherent misalignment prevents establishing explicit mesh-level correspondences, forcing prior work to rely on indirect supervision signals such as SDF or rendering losses. Consequently, fine geometric details, particularly sharp features, are poorly preserved during reconstruction. To address this, we introduce TopoMesh, a sparse voxel-based VAE that unifies both GT and predicted meshes under a shared Dual Marching Cubes (DMC) topological framework. Specifically, we convert arbitrary input meshes into DMC-compliant representations via a remeshing algorithm that preserves sharp edges using an L∞ distance metric. Our decoder outputs meshes in the same DMC format, ensuring that both predicted and target meshes share identical topological structures. This establishes explicit correspondences at the vertex and face level, allowing us to derive explicit mesh-level supervision signals for topology, vertex positions, and face orientations with clear gradients. Our sparse VAE architecture employs this unified framework and is trained with Teacher Forcing and progressive resolution training for stable and efficient convergence. Extensive experiments demonstrate that TopoMesh significantly outperforms existing VAEs in reconstruction fidelity, achieving superior preservation of sharp features and geometric details.

Generating detailed and seamless textures for 3D meshes remains an open challenge. Recent image and video generation models, empowered by large-scale visual priors, are capable of producing highly detailed images and are thus promising for multi-view texture synthesis. However, evaluating texture quality involves multiple dimensions beyond visual fidelity. Multi-view back-projection often introduces seams and inconsistencies between different views or near occluded regions, while direct generation on UV-unwrapped maps suffers from UV distortions and ambiguities.Generating textures directly in 3D space offers an inherent advantage in ensuring continuity and spatial coherence, making it a critical and worthwhile research direction. Therefore, we systematically investigate 3D-native texture generation from the perspectives of representation and generation, and present current best practices for this approach.To this end, we employ a local vector field with a structured latent representation to model the joint distribution of texture and geometry. This design enables texture generation conditioned on high-fidelity geometric features within a unified latent space. Crucially, our approach is inherently free from occlusion artifacts, multi-view inconsistencies, and UV-related distortions caused by fragmented surface parameterizations. Extensive experiments demonstrate that our method produces high-quality, seamless textures and supports flexible downstream tasks such as editing and inpainting, marking a significant step forward in 3D-native texture generation.

Generating high-fidelity, seamless textures directly on 3D surfaces, what we term 3D-native texturing, remains a fundamental open challenge, with the potential to overcome long-standing limitations of UV-based and multi-view projection methods. However, existing native approaches are constrained by the absence of a powerful and versatile latent representation, which severely limits the fidelity and generality of their generated textures. We identify this representation gap as the principal barrier to further progress. We introduce LaFiTe, a framework that addresses this challenge by learning to generate textures as a 3D generative sparse latent color field. At its core, LaFiTe employs a variational autoencoder (VAE) to encode complex surface appearance into a sparse, structured latent space, which is subsequently decoded into a continuous color field. This representation achieves unprecedented fidelity, exceeding state-of-the-art methods by >10 dB PSNR in reconstruction, by effectively disentangling texture appearance from mesh topology and UV parameterization. Building upon this strong representation, a conditional rectified-flow model synthesizes high-quality, coherent textures across diverse styles and geometries. Extensive experiments demonstrate that LaFiTe not only sets a new benchmark for 3D-native texturing but also enables flexible downstream applications such as material synthesis and texture super-resolution, paving the way for the next generation of 3D content creation workflows.

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 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) 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) 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) 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.