Knowledge catalogue
arXiv:2607.05376v1 Announce Type: new Abstract: Recent advances in video diffusion models have enabled either long single-view generation through temporal autoregression, or short multi-view synthesis
arXiv:2607.03985v1 Announce Type: cross Abstract: Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Spea
arXiv:2602.23297v2 Announce Type: replace Abstract: Medical diagnosis requires the effective synthesis of visual manifestations and clinical metadata. However, existing methods often treat metadata as
arXiv:2607.02721v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis, but practical scenes often contain millions of Gaussians, making compr
arXiv:2607.03069v1 Announce Type: new Abstract: As video generation paradigms evolve from localized manipulation to full-scene synthesis, AI-generated video detection becomes increasingly challenging,
arXiv:2607.02628v1 Announce Type: new Abstract: While diffusion models have revolutionized image synthesis, their application to real-world inverse problems is often hampered by the need for massive d
arXiv:2607.01698v2 Announce Type: replace Abstract: 3D Gaussian Splatting has demonstrated remarkable potential in novel view synthesis. In contrast to small-scale scenes, large-scale scenes inevitabl
arXiv:2607.03390v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has revolutionized novel-view synthesis with its fast and high-fidelity rendering. However, rendering at high FPS and low l
arXiv:2607.01238v1 Announce Type: cross Abstract: Recent advances in speech synthesis have shifted from phoneme representations to direct grapheme modeling. While phonemes address the one-to-many mapp
arXiv:2607.01061v1 Announce Type: new Abstract: Computer-assisted synthesis planning breaks target molecules into accessible precursors using large libraries of reaction rules that assign each transfo
arXiv:2607.00885v1 Announce Type: cross Abstract: Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis, enabl
arXiv:2603.16085v2 Announce Type: replace-cross Abstract: Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets. However, generating 3D compositional obje
arXiv:2603.08997v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) achieves real-time novel-view synthesis by optimizing millions of anisotropic Gaussians, yet its training remains expen
arXiv:2607.01105v1 Announce Type: new Abstract: We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synth
arXiv:2606.30949v1 Announce Type: new Abstract: High-Level Synthesis (HLS) provides a fast path from concepts to silicon, but converting real-world software into synthesizable HLS code remains challen
arXiv:2603.17461v2 Announce Type: replace Abstract: Streaming autoregressive (AR) video generators combined with few-step distillation achieve low-latency, high-quality synthesis, yet remain difficult
arXiv:2603.12893v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a standard technique for post-training diffusion-based image synthesis models, as it enables learning f
arXiv:2603.24036v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) enables real-time, photorealistic novel view synthesis, making it a highly attractive representation for model-based vi
arXiv:2606.31259v1 Announce Type: cross Abstract: Diffusion-based text-to-audio (TTA) models achieve impressive synthesis quality but suffer from high inference latency due to iterative multi-step den
arXiv:2603.28049v3 Announce Type: replace Abstract: Autoregressive (AR)-Diffusion hybrid paradigms combine AR's structured semantic modeling with diffusion's high-fidelity synthesis, yet suffer from a
arXiv:2506.07069v2 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, combining high-quality reconstruction with efficien
arXiv:2606.28828v1 Announce Type: new Abstract: Learning a 4D scene representation from a single monocular video that supports dynamic novel-view synthesis while maintaining faithful geometry over tim
arXiv:2606.29333v1 Announce Type: new Abstract: Uncalibrated volumetric video streaming for human reconstruction is essential for holographic communication and AR/VR, yet remains challenging due to th
arXiv:2606.28362v1 Announce Type: cross Abstract: Systematic reviews and meta-analyses (SR/MA) remain the gold standard for evidence synthesis, yet completing one typically requires 67 weeks and subst
arXiv:2606.30017v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting have demonstrated unprecedented success in novel view synthesis. However, the substantial inference and storage
arXiv:2603.25743v2 Announce Type: replace Abstract: Reference-to-video (R2V) generation is a controllable video synthesis paradigm that constrains the generation process using both text prompts and re
arXiv:2606.30545v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has achieved remarkable success in real-time novel view synthesis, yet it suffers from severe overfitting under sparse-view
arXiv:2606.28194v1 Announce Type: new Abstract: While interpretable models such as concept bottleneck models (CBMs) and program synthesis methods enable verification of model decisions, their evaluati
arXiv:2511.15022v2 Announce Type: replace Abstract: Complex-valued Gaussian primitives have recently been explored for representing holographic radiance fields in 3D novel view synthesis. In this work
arXiv:2606.26754v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) provides an efficient and explicit representation for novel view synthesis, enforcing stylistic coherence across view
arXiv:2606.27002v1 Announce Type: cross Abstract: This paper presents a deep learning-assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA). Convo
arXiv:2606.26857v1 Announce Type: new Abstract: The interpretation phase of life cycle assessment often lacks structured mechanisms for translating quantified improvement opportunities addressing envi
arXiv:2606.26171v1 Announce Type: cross Abstract: Recent image generation models achieve impressive quality in single-image synthesis, but often fail to maintain consistency across sequential outputs,
arXiv:2606.27089v1 Announce Type: new Abstract: Modern image generation model rapidly grows their sizes to meet high-fidelity image synthesis. However, they gradually become unaffordable for their eno
arXiv:2606.26534v1 Announce Type: cross Abstract: Recently, zero-shot text-to-speech (TTS) has enabled high-fidelity and expressive speech synthesis, but it often fails to imitate unseen speaking styl
arXiv:2507.09052v3 Announce Type: replace Abstract: Training data for class-conditional image synthesis often exhibit a long-tailed distribution with limited amount of images for tail classes. Such an
arXiv:2509.19297v3 Announce Type: replace Abstract: Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective solution for novel view synthesis. Existing methods predominantly rely o
arXiv:2603.15510v2 Announce Type: replace Abstract: The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification. While Large Language Models (LLMs) show
arXiv:2606.23280v1 Announce Type: new Abstract: Automated Reward Design (ARD) aims to replace manual reward engineering in reinforcement learning with language-driven reward function synthesis. Howeve
arXiv:2606.22216v1 Announce Type: cross Abstract: While longitudinal brain PET imaging is the gold standard for quantifying the spatiotemporal accumulation of Beta-amyloid, its widespread clinical uti
arXiv:2503.12947v3 Announce Type: replace Abstract: Neural Radiance Field (NeRF) has shown remarkable performance in novel view synthesis but requires numerous multi-view images, limiting its practica
arXiv:2606.23682v1 Announce Type: new Abstract: Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis.
arXiv:2606.22550v1 Announce Type: new Abstract: Autoregressive visual models (AVMs) based on next-scale prediction have emerged as a prominent paradigm for image and video synthesis. However, decompos
arXiv:2606.21700v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) aims to learn a target-domain classifier from labeled source data and unlabeled target data under distribution shif
arXiv:2606.20731v1 Announce Type: new Abstract: Large-scale human motion datasets are essential for training robust motion models for analysis, synthesis, and understanding. While marker-based motion
arXiv:2606.11783v1 Announce Type: new Abstract: Recent progress in video generation has shown impressive visual synthesis capabilities. However, open-domain customized video generation remains limited
arXiv:2606.11361v1 Announce Type: cross Abstract: Structured abstracts are important for biomedical literature processing, by facilitating information retrieval, text mining, and knowledge synthesis.
arXiv:2606.11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions. Yet, their abili
arXiv:2606.12106v1 Announce Type: cross Abstract: This paper presents our solution to the 2026 SoccerNet VQA Challenge. We first develop a cost-effective data synthesis pipeline driven by a Vision-Lan
arXiv:2606.11841v1 Announce Type: new Abstract: Low-light novel view synthesis is challenging because dark multi-view images contain noise, weak structural detail, and compressed dynamic range. Recent
arXiv:2606.11348v1 Announce Type: new Abstract: Clock Tree Synthesis (CTS) is a computationally expensive stage in the physical design flow, requiring iterative EDA tool invocations to navigate a vast
arXiv:2606.11662v1 Announce Type: new Abstract: Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis. A central challenge
arXiv:2606.12263v1 Announce Type: new Abstract: While Latent Diffusion Models (LDMs) have revolutionized visual synthesis, they are increasingly exploited for unauthorized mimicry of individuals. Exis
arXiv:2606.11529v1 Announce Type: cross Abstract: Point-based differentiable rendering underpins modern 3D reconstruction, novel-view synthesis, and learning-based graphics pipelines, but developing n
arXiv:2606.10988v1 Announce Type: new Abstract: While recent advancements in generative AI have substantially accelerated static 3D model creation workflows, the synthesis of category-agnostic 3D anim
arXiv:2502.06819v2 Announce Type: replace Abstract: This paper presents a framework for generating 3D indoor scenes from text prompts. Existing methods often formulate scene synthesis as an object lay
arXiv:2606.09794v1 Announce Type: new Abstract: View-dependent appearance modeling remains a challenging problem in novel-view synthesis and reconstruction. Accurately representing complex angular eff
arXiv:2208.00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowshee
arXiv:2606.07932v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become an efficient explicit representation for radiance field reconstruction and real-time novel view synthesis. Howev
arXiv:2606.09034v1 Announce Type: new Abstract: Neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) are two mainstream approaches for novel view synthesis. They often show complementary perf