24 accepted papers
Recent progress in multimodal generation has increasingly combined autoregressive (AR) and diffusion-based approaches, leveraging their complementary strengths: AR models capture long-range dependencies and produce fluent, context-aware outputs, while diffusion models operate in continuous latent sp…
Skeleton generation is essential for animating 3D assets, but current deep learning methods remain limited: they cannot handle the growing structural complexity of modern models and offer minimal controllability, creating a major bottleneck for real-world animation workflows. To address this, we pro
Controllable video generation (CVG) has advanced rapidly, yet current systems falter when more than one actor must move, interact, and exchange positions under noisy control signals. We address this gap with DanceTogether, the first end-to-end diffusion framework that turns a single reference image…
Apparel is a fundamental component of human appearance, making garment digitalization critical for digital human creation. However, sewing pattern creation traditionally relies on the intuition and extensive experience of skilled artisans. This manual bottleneck significantly hinders the scalability…
Recent methods have made notable progress in the visual quality of hand-object interaction video synthesis. However, most approaches rely on 2D control signals that lack spatial expressiveness and limit the utilization of synthetic 3D conditional data. To address these limitations, we propose HVG-3D
"Fedspeak", the stylized and often nuanced language used by the U.S. Federal Reserve, encodes implicit policy signals and strategic stances. The Federal Open Market Committee strategically employs Fedspeak as a communication tool to shape market expectations and influence both domestic and global e
Despite rapid progress in video generation, existing models are incapable of producing vector animation, a dominant and highly expressive form of multimedia on the Internet. Vector animations offer resolution-independence, compactness, semantic structure, and editable parametric motion representatio
Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal. However, animating these generated characters remains a challenging task, especially for complex elements like garments and hair, due to the lack of large-scale datasets and effective rigging…
Tracking a patient’s cognitive status early in the onset of the disease provides an opportunity to diagnose and intervene in Alzheimer’s disease (AD). However, relying solely on magnetic resonance imaging (MRI) images with traditional classification and regression models may not fully extract finer-…
Recently, advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of a robust benchmark specifically for assessing the image‐to‐web conversion proficiency of these large models. It is essential to ensure…
With the success of 2D diffusion models, 2D AIGC content has already transformed our lives. Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text. However, we argue that current 3D AIGC methods still don’t fully un…
Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. Examining jailbreak prompts helps uncover the shortcomings of LLMs. However, current jailbreak methods and the target model’s defenses are…
This work focuses on multi-floor indoor exploration, which remains an open area of research. Compared to traditional methods, recent learning-based explorers have demonstrated significant potential due to their robust environmental learning and modeling capabilities, but most are restricted to 2D en
As large language models (LLMs) advance across diverse tasks, the need for comprehensive evaluation beyond single metrics becomes increasingly important.To fully assess LLM intelligence, it is crucial to examine their interactive dynamics and strategic behaviors.We present LLMsPark, a game theory–ba
Large Language Models (LLMs) have demonstrated significant capabilities, particularly in the domain of question answering (QA). However, their effectiveness in QA is often undermined by the vagueness of user questions. To address this issue, we introduce single-round instance-level prompt optimizat…
Recent approaches for visually-rich document understanding (VrDU) uses manually annotated semantic groups, where a semantic group encompasses all semantically relevant but not obviously grouped words. As OCR tools are unable to automatically identify such grouping, we argue that current VrDU approac…
Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GPT-4 excel in medical question answering but may face challenges in the lack of interpretability when handling complex ta…
Audio Description (AD) aims to generate narrations of information that is not accessible through unimodal hearing in movies to aid the visually impaired in following film narratives. Current solutions rely heavily on manual work, resulting in high costs and limited scalability. While automatic metho…
N:M sparsity has received increasing attention due to its remarkable performance and latency trade-off compared with structured and unstructured sparsity. However existing N:M sparsity methods do not differentiate the relative importance of weights among blocks and leave important weights underappre…
Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers which was inefficient and lacked generalized representation due to the scarcity of multimodal data. Therefore recent studies have ut…
Processing and reasoning over long contexts is crucial for many practical applications of Large Language Models (LLMs), such as document comprehension and agent construction. Despite recent strides in making LLMs process contexts with more than 100K tokens, there is currently a lack of a standardize…
Most offline video instance segmentation (VIS) methods lack consideration for multi-scale spatio-temporal features, which leads to unstable instance association across frames. To address this problem, we propose IAST that builds Instance Association relying on Spatio-Temporal features for video inst…
The attention mechanism in Transformer-based HOI models plays important role in the comprehension of human and object interaction. However, most previous Transformer-based models ignore the guidance on the query and attention, which leads to a poor understanding of interaction behaviour. In this pap…
Most video instance segmentation(VIS) models only focused on visual knowledge and ignored intrinsic linguistic knowledge. Based on the observation that incorporating linguistic knowledge can significantly improve the model’s contextual understanding of the video, in this paper, we present a Video In…