Search: “synthesis”

arXiv:2608.05249v1 Announce Type: cross Abstract: Real-world multimodal instructions often bundle multiple requirements with unequal importance, yet most multimodal training data still reduce instruct

arXiv:2608.04412v1 Announce Type: new Abstract: High-quality driving data are essential for autonomous-driving systems and generative world models. However, rare and safety-critical scenarios involvin

arXiv:2608.03822v1 Announce Type: cross Abstract: Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generat

arXiv:2508.04551v2 Announce Type: replace Abstract: While recent advances in virtual try-on (VTON) have achieved realistic garment transfer to human subjects, its inverse task, virtual try-off (VTOFF)

arXiv:2608.02580v1 Announce Type: new Abstract: Learning generalizable robot manipulation policies requires large-scale and diverse demonstration data. Egocentric human manipulation videos offer rich

arXiv:2608.02177v1 Announce Type: new Abstract: Existing rainfall simulation methods for autonomous driving remain limited in physical controllability and multi-view consistency. This paper presents G

arXiv:2608.01973v1 Announce Type: cross Abstract: Existing indoor layout generators produce globally plausible layouts yet may retain local violations such as collisions, out-of-bounds placements, obs

arXiv:2606.13097v2 Announce Type: replace-cross Abstract: Code-writing large language models (CodeLLMs) generate executable code policies for embodied agents by translating natural language goals and

arXiv:2512.11654v3 Announce Type: replace Abstract: The acquisition cost for large, annotated motion datasets remains a critical bottleneck for skeletal-based Human Activity Recognition (HAR). Althoug

arXiv:2607.25554v1 Announce Type: new Abstract: Future event prediction carries broad social impact yet remains challenging. SOTA approaches augment LLMs with external agent frameworks whose predictiv

Siri Expressive Voices synthesize rich, configurable speech in real time and entirely on device, powered by AFM 3 Core Advanced, Apple’s most powerful on-device foundation model. This work presents th

arXiv:2509.21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy. It offers real time anatomical visua

arXiv:2607.20499v1 Announce Type: new Abstract: Large Language Models generate plausible backend code, but a single-pass paradigm provides no guarantee of correctness or runtime reliability. We presen

arXiv:2607.21448v1 Announce Type: new Abstract: Dynamic scene reconstruction with 3D Gaussian Splatting requires a balance between fine-grained motion modeling, structural stability, and compact repre

arXiv:2607.09530v2 Announce Type: replace Abstract: We introduce Freya-TTS, a compact, tokenizer-free, Turkish-first text-to-speech model designed for highly reliable and efficient conversational synt

arXiv:2607.19522v1 Announce Type: new Abstract: While machine learning-based weather models hold significant promise, they struggle to predict the detailed structure of large-scale weather systems suc

arXiv:2606.16241v3 Announce Type: replace Abstract: Visual anagram is an intriguing form of art creation wherein a single image presents different conceptual interpretations under transformations such

arXiv:2607.08201v1 Announce Type: cross Abstract: Large-vocabulary instance segmentation is constrained by long-tailed category distributions and fine-grained inter-class ambiguity. While data synthes

arXiv:2607.07601v1 Announce Type: cross Abstract: Safety evaluation for autonomous driving is dominated by rare, safety-critical interactions, motivating simulators that can deliberately synthesize co

arXiv:2607.05775v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and ope

arXiv:2607.06160v1 Announce Type: cross Abstract: Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large language models (LLMs

arXiv:2602.00846v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) struggle with alignment due to the limitations of existing reward models (RMs), which are predominantly vis

arXiv:2607.05837v1 Announce Type: new Abstract: Defocus blur degrades fine image structures and limits visual perception, which can adversely affect downstream vision tasks. Although recent deep learn

arXiv:2607.06234v1 Announce Type: new Abstract: Generating CT volumes from MRI and CBCT can improve treatment planning in adaptive radiotherapy while avoiding additional radiation exposure. However, d

arXiv:2512.11797v2 Announce Type: replace-cross Abstract: The collection of large-scale and diverse robot demonstrations remains a major bottleneck for imitation learning, as real-world data acquisiti

arXiv:2607.05325v1 Announce Type: new Abstract: Cerebral microbleeds (CMBs) are MRI markers of small vessel disease and the microbleed component of amyloid related imaging abnormalities (ARIA-H), but

arXiv:2506.20995v4 Announce Type: replace Abstract: We propose a step-by-step video-to-audio (V2A) generation method that provides finer control over the generation process and more realistic audio sy

arXiv:2606.29181v1 Announce Type: cross Abstract: Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to

arXiv:2603.14161v2 Announce Type: replace Abstract: Many disciplines need quantitative models that synthesize experimental data across multiple instances of the same general system. For example, neuro

arXiv:2606.28401v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong performance in visual understanding, yet they still suffer from hallucinations, generating content that

arXiv:2606.28126v1 Announce Type: new Abstract: This article addresses the combinatorial complexity inherent in modern high-tech system design by presenting automation-in-design (AiD) as a transformat

arXiv:2410.02103v4 Announce Type: replace Abstract: Recent works in volume rendering, extit{e.g.} NeRF and 3D Gaussian Splatting (3DGS), significantly advance the rendering quality and efficiency with

arXiv:2606.26764v1 Announce Type: cross Abstract: Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shif

arXiv:2606.26397v1 Announce Type: cross Abstract: Real-world decision-making often requires balancing multiple conflicting objectives, a challenge that standard Reinforcement Learning (RL) frequently

arXiv:2606.25390v1 Announce Type: new Abstract: Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Ana

arXiv:2606.25465v1 Announce Type: new Abstract: While image stylization has been studied extensively, video stylization remains a critical and largely unsolved challenge in the field of intelligent co

arXiv:2606.24072v1 Announce Type: new Abstract: Fabric moire is a sampling-induced aliasing artifact caused by the interaction between fine textile patterns and camera sensor grids, producing structur

arXiv:2602.19330v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are increasingly explored for physical design analysis in Electronic Design Automation, particularly for modeling Clock

arXiv:2606.09767v1 Announce Type: cross Abstract: Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractiv

arXiv:2606.08938v1 Announce Type: cross Abstract: Clinical diagnosis requires flexible use of multiple reasoning paradigms under incomplete patient information. Existing LLM-based medical agents show

arXiv:2403.00965v2 Announce Type: replace-cross Abstract: Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the perf

arXiv:2606.05252v1 Announce Type: cross Abstract: Security teams routinely simulate attacks against their own systems to check whether their monitoring would catch a real intruder. These Breach-and-At

arXiv:2606.06133v1 Announce Type: cross Abstract: TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols. Large language models (LLMs) frequently produ

arXiv:2606.06048v1 Announce Type: new Abstract: Pathological gait datasets remain scarce due to privacy, recruitment, cost, and movement variability. Our work presents a multimodal LLM-guided framewor

arXiv:2606.02908v1 Announce Type: cross Abstract: Multi-turn user-facing agents must infer user intent from incomplete requests, collect missing information through dialogue and tools, and execute val

arXiv:2606.02434v1 Announce Type: new Abstract: Precise parametric control over circuit geometry is essential for semiconductor inspection, yet obtaining sufficient real training data remains costly.