6556 papers
This paper introduces EduAIGV-1k, the first benchmark dataset for evaluating AI-generated educational videos on math concepts, and proposes EduVQA, a novel assessment framework featuring a Structured 2D Mixture-of-Experts module that leverages fine-grained perceptual and prompt-alignment annotations to outperform existing video quality baselines.
The paper introduces TikZilla, a family of small open-source models trained on the high-quality DaTikZ-V4 dataset using a two-stage pipeline of supervised fine-tuning and reinforcement learning with image-based rewards, which significantly outperforms prior approaches and matches GPT-5 in generating accurate TikZ figures from text descriptions.
This paper introduces TinyIceNet, a compact, low-power semantic segmentation network co-designed for FPGA deployment that enables near-real-time, energy-efficient sea ice mapping directly on-board satellites using Sentinel-1 SAR imagery.
The paper proposes AWDiff, a novel diffusion-based framework that integrates the a trous wavelet transform and BioMedCLIP semantic conditioning to generate high-fidelity lung ultrasound images while preserving critical fine-scale diagnostic structures often lost in existing methods.
This paper proposes RL3DEdit, a reinforcement learning framework that leverages 3D foundation model priors from VGGT to generate novel rewards for optimizing 2D diffusion-based editing, thereby achieving stable multi-view consistency in 3D scene editing without requiring paired training data.
Kling-MotionControl is a unified DiT-based framework that achieves robust, high-fidelity holistic character animation by orchestrating specialized motion representations for body, face, and hands while ensuring cross-identity generalization, faithful appearance preservation, and over 10x faster inference through advanced distillation.
To address the trade-off between safety and image quality in Text-to-Image models, the authors propose Conditioned Activation Transport (CAT), a framework that utilizes a contrastive dataset and geometry-based nonlinear transport maps to steer unsafe activations without degrading benign generations.
This paper proposes MoD-DPO, a Modality-Decoupled Direct Preference Optimization framework that mitigates cross-modal hallucinations in omni-modal LLMs by enforcing modality-specific invariance and sensitivity through regularization and language-prior debiasing, thereby significantly improving perception accuracy and hallucination resistance.
This paper introduces CoWVLA, a novel Vision-Language-Action framework that unifies world-model temporal reasoning with disentangled latent motion representations to enable efficient and accurate visuomotor learning by predicting continuous latent motion chains and aligning them with discrete action sequences.
This paper introduces ACE-Brain-0, a generalist foundation brain that leverages spatial intelligence as a universal scaffold and employs a Scaffold-Specialize-Reconcile (SSR) paradigm to unify diverse embodied tasks like autonomous driving and robotics within a single multimodal large language model, achieving state-of-the-art performance across 24 benchmarks.
COP-GEN is a multimodal latent diffusion transformer designed for Earth observation that addresses the inherent non-injectivity of cross-sensor relationships by modeling conditional distributions to generate diverse, physically consistent, and uncertainty-aware realizations across optical, radar, and elevation modalities without task-specific retraining.
The paper introduces UniG2U-Bench, a comprehensive benchmark evaluating generation-to-understanding across 30 subtasks, revealing that while unified multimodal models generally underperform base vision-language models, they offer specific advantages in spatial intelligence and multi-round reasoning, thereby highlighting the need for improved training data and paradigms.
DuoMo is a state-of-the-art generative method that reconstructs globally consistent, world-space human motion from unconstrained and noisy videos by employing a dual diffusion framework that first estimates camera-space motion and then refines it into a coherent world-space trajectory without relying on parametric models.
LoGeR introduces a novel architecture featuring a hybrid memory module that combines parametric Test-Time Training and non-parametric Sliding Window Attention to enable dense 3D geometric reconstruction of extremely long video sequences with global consistency, significantly outperforming existing feedforward methods without requiring post-optimization.
This paper presents controlled from-scratch pretraining experiments using the Transfusion framework to demonstrate that a Mixture-of-Experts architecture effectively harmonizes the data-scaling asymmetry between vision and language, enabling unified multimodal models with superior world modeling and complementary capabilities.
Tether is an autonomous robotic framework that leverages correspondence-driven trajectory warping and vision-language models to generate diverse, high-quality functional play data from minimal demonstrations, enabling the continuous improvement of imitation policies to expert-level performance.
The paper introduces ULTRA, a unified framework that combines physics-driven neural retargeting with a multimodal reinforcement learning controller to enable autonomous, goal-conditioned whole-body loco-manipulation on humanoids using sparse high-level task specifications and noisy visual inputs, eliminating the need for predefined motion references.
This paper presents a two-stage learning framework for fine-grained robotic manipulation tasks like peeling, which combines force-aware imitation learning with preference-based finetuning to achieve over 90% success rates and strong zero-shot generalization by aligning robot behavior with human qualitative preferences.
This paper introduces MIBURI, a novel online causal framework that leverages body-part aware gesture codecs and LLM-based speech embeddings to generate expressive, diverse, and real-time synchronized full-body gestures and facial expressions for Embodied Conversational Agents, overcoming the rigidity and latency limitations of existing methods.
The paper introduces Utonia, a self-supervised point transformer encoder that unifies diverse 3D point cloud domains into a single consistent representation space, thereby enhancing perception capabilities and enabling emergent benefits for embodied and multimodal reasoning tasks.