Video generation has emerged as a promising tool for world simulation, leveraging visual data to replicate real-world environments. Within this context, egocentric video generation, which centers on the human perspective, holds significant potential for enhancing applications in virtual reality, augmented reality, and gaming. However, the generation of egocentric videos presents substantial challenges due to the dynamic nature of egocentric viewpoints, the intricate diversity of actions, and the complex variety of scenes encountered. Existing datasets are inadequate for addressing these challenges effectively. To bridge this gap, we present EgoVid-5M, the first high-quality dataset specifically curated for egocentric video generation. EgoVid-5M encompasses 5 million egocentric video clips and is enriched with detailed action annotations, including 5M high-level textual descriptions and 65K fine-grained kinematic control annotations. To ensure the integrity and usability of the dataset, we implement a sophisticated data cleaning pipeline designed to maintain frame consistency, action coherence, and motion smoothness under egocentric conditions. Furthermore, we introduce EgoDreamer, which is capable of generating egocentric videos driven simultaneously by action descriptions and kinematic control signals. The EgoVid-5M dataset, associated action annotations, and all data cleansing metadata will be released for the advancement of research in egocentric video generation.

We propose LiteReality, a novel pipeline that converts RGB-D scans of indoor environments into compact, realistic, and interactive 3D virtual replicas. LiteReality not only reconstructs scenes that visually resemble reality but also supports key features essential for graphics pipelines--such as object individuality, articulation, high-quality physically based rendering materials. At its core, LiteReality first performs scene understanding and parses the results into a coherent 3D layout and objects, with the help of a structured scene graph.

Novel view synthesis (NVS) is crucial in computer vision and graphics, with wide applications in AR, VR, and autonomous driving. While 3DGaussian Splatting (3DGS) enables real-time rendering with high appearance fidelity, it suffers from multi-view inconsistencies, limiting geometric accuracy. In contrast, 2DGaussian T Splatting o address (2DGS) these limitations, enforces multi-vie we propose w consistenc Exchangeable y but compromises Gaussian Splatting texture (EGGS), details. a hybrid representation that integrates 2D and 3DGaussians to balance appearance and geometry. To achieve this, we introduce Hybrid Gaussian Rasterization for unified rendering, Adaptive Type Exchange for dynamic adaptation between 2D and 3DGaussians, and Frequency-Decoupled Optimization that effectively exploits the implementation strengths of ensures each type efficient of Gaussian training representation.

We present GOATEX, a diffusion-based method for 3D mesh texturing that generates methods high-quality perform te well xtures on for visible both re exterior gions, and they interior inherently surfaces.

Photorealistic Codec Avatars (PCA), which generate high-fidelity human face renderings, are increasingly being used in Virtual Reality (VR) environments to enable immersive communication and interaction through deep learning-based generative models. However, these models impose significant computational demands, making real-time inference challenging on resource-constrained VR devices such as head-mounted displays (HMDs), where latency and power efficiency are critical. To address this challenge, we propose an efficient post-training quantization (PTQ) method tailored for Codec Avatar models, enabling low-precision execution without compromising output quality. In addition, we design a custom hardware accelerator that can be integrated into the system-on-chip (SoC) of VR devices to further enhance processing efficiency. Building on these components, we introduce ESCA, a full-stack optimization framework that accelerates PCA inference on edge VR platforms. Experimental results demonstrate that ESCA boosts FovVideoVDP quality scores by up to +0.39 over the best 4-bit baseline, delivers up to 3.36 latency reduction, and sustains a rendering rate of 100 frames per second in endto-end tests, satisfying real-time VR requirements. These results demonstrate the feasibility of deploying high-fidelity codec avatars on resource-constrained devices, opening the door to more immersive and portable VR experiences. Paper website can be found at https://zmzfpc.github.io/ESCA/.

To mitigate the distortions brought by equirectangular projection, existing methods typically divide 360 images into distortion-less perspective patches. However, since these patches are processed independently, depth inconsistencies are often introduced due to scale drift among patches. Recently, video depth estimation (VDE) models have leveraged temporal consistency for stable depth predictions across frames. Inspired by this, we propose to represent a 360 image as a sequence of perspective frames, mimicking the viewpoint adjustments users make when exploring a 360 scenario in virtual reality. Thus, the spatial consistency among perspective depth patches can be enhanced by exploiting the temporal consistency inherent in VDE models. To this end, we introduce a training-free pipeline for 360 monocular depth estimation, called ST2360D.

The company also debuted a new platform for brands wanting to build their own AI glasses. High-end augmented reality and mixed reality devices are set to get a boost thanks to Qualcomm's latest XR chip. During a keynote at Augmented World Expo (AWE), the company unveiled its Snapdragon Reality Elite processor, which it says will allow the next generation of AR and mixed reality headsets to be smaller and more efficient. In terms of specs, the Snapdragon Reality Elite can support up to 4.4K resolution in each eye at 90 fps, a modest upgrade from the XR2+ Gen 2, but one that Qualcomm says will enable better image quality and lower latency. It also delivers significant improvements in terms of efficiency, with up to 20 percent boost in battery life while running up to 12 degrees Celsius (about 54 degrees Fahrenheit) cooler, compared with the XR2+ Gen 2. Performance-wise, Reality Elite comes with notable gains over the previous generation as well.

You Can Finally Buy Snap's New AR Specs--for $2,195 Snap CEO Evan Spiegel lays out the company's vision for its augmented-reality smart glasses, arriving later this year. Snap--maker of the popular social app Snapchat--has a new pair of augmented-reality smart glasses called Specs. Snap CEO Evan Spiegel revealed the new glasses at an event during the Augmented World Expo (AWE) tech conference in Long Beach, California. As Snap frames it, this isn't a prototype or developer device--it's the first actual consumer version of the Specs AR glasses, unlike the previous generation exclusively sold to developers and creators. Snap says it expects the devices to ship this fall in the US, UK, and France.

In the field of computer vision, the task of stereo image super-resolution (StereoSR) has garnered significant attention due to its potential applications in augmented reality, virtual reality, and autonomous driving. Traditional Transformer-based models, while powerful, often suffer from attention noise, leading to suboptimal reconstruction issues in super-resolved images. This paper introduces DIFFSSR, a novel neural network architecture designed to address these challenges. We introduce the Diff Cross Attention Block (DCAB) and the Sliding Stereo Cross-Attention Module (SSCAM) to enhance feature integration and mitigate the impact of attention noise.

Photorealistic Codec Avatars (PCA), which generate high-fidelity human face renderings, are increasingly being used in Virtual Reality (VR) environments to enable immersive communication and interaction through deep learning-based generative models. However, these models impose significant computational demands, making real-time inference challenging on resource-constrained VR devices such as head-mounted displays (HMDs), where latency and power efficiency are critical. To address this challenge, we propose an efficient post-training quantization (PTQ) method tailored for Codec Avatar models, enabling low-precision execution without compromising output quality. In addition, we design a custom hardware accelerator that can be integrated into the system-on-chip (SoC) of VR devices to further enhance processing efficiency. Building on these components, we introduce ESCA, a full-stack optimization framework that accelerates PCA inference on edge VR platforms. Experimental results demonstrate that ESCA boosts FovVideoVDP quality scores by up to +0.39 over the best 4-bit baseline, delivers up to 3.36 latency reduction, and sustains a rendering rate of 100 frames per second in end-to-end tests, satisfying real-time VR requirements. These results demonstrate the feasibility of deploying high-fidelity codec avatars on resource-constrained devices, opening the door to more immersive and portable VR experiences.