Spherical Geometry=Aware Transformer for PAnoramic Semantic Segmentation

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2. PanoGRF: Generalizable Spherical Radiance Fields PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas Achieving an immersive enabling experience users to explore virtual environments with six degrees of freedom (6DoF) is essential for various applications such as virtual reality (VR). Wide-baseline panoramas are commonly used in these applications to reduce network bandwidth and storage requirements. However , synthesizing novel views from […]...
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5. Optimizing and deploying transformer INT8 inference Transformer-based models have revolutionized the natural language processing (NLP) domain. Ever since its inception, transformer architecture has been integrated into models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) for performing tasks such as text generation or summarization and question and answering to name a few. The newer models are getting […]...
6. optimize large scale transformer model inference Large-scale transformer models, such as GPT-2 and GPT-3, are among the most useful self-supervised transformer language models for natural language processing tasks such as language translation, question answering, passage summarization, text generation, and so on. After successfully shipping the first deep learning model for IntelliCode completion, our recent research effort brings GPT-C, a multi-layer generative decoder […]...
7. SparseGNV SparseGNV: Generating Novel Views of Indoor Scenes with Sparse RGB-D Images We study to generate novel views of indoor scenes given sparse input views. The challenge is to achieve both photorealism and view consistency. We present SparseGNV: a learning framework that incorporates 3D structures and image generative models to generate novel views with three modules. […]...
8. PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding Recent advances in text-to-image generation have made remarkable progress in synthesizing realistic human photos conditioned on given text prompts. However, existing personalized generation methods cannot simultaneously satisfy the requirements of high efficiency, promising identity (ID) fidelity, and flexible text controllability. In this work, we introduce PhotoMaker, an efficient personalized text-to-image generation method, which mainly encodes an […]...
9. Semantic Verbal Memory Deficit Semantic Verbal Memory Deficit is a condition that affects a person’s ability to remember and understand the meaning of words and concepts. In simple terms, it can make it difficult for someone to remember the meanings of words, phrases, or even common knowledge. This article will provide a straightforward explanation of what Semantic Verbal Memory […]...
10. Speech2Lip: High-fidelity Speech to Lip Generation Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video Synthesizing realistic videos according to a given speech is still an open challenge. Previous works have been plagued by issues such as inaccurate lip shape generation and poor image quality. The key reason is that only motions and appearances on limited facial areas […]...
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12. Sparse3D Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views Reconstructing 3D objects from extremely sparse views is a long-standing and challenging problem. While recent techniques employ image diffusion models for generating plausible images at novel viewpoints or for distilling pre-trained diffusion priors into 3D representations using score distillation sampling (SDS), these methods often struggle to […]...
13. Partial Semantic Verbal Memory Deficit Partial Semantic Verbal Memory Deficit, or PSVMD for short, is a condition that affects a person’s ability to remember and use words and their meanings. In simpler terms, it can make it challenging to recall and understand certain words, even if you’ve known them before. In this article, we will break down PSVMD, exploring its […]...
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15. NeRF-Texture: Texture Synthesis With Neural Radiance Fields NeRF-Texture: Texture Synthesis With Neural Radiance Fields Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only […]...