7843 papers
DrivePTS is a progressive learning framework that enhances autonomous driving scene generation by mitigating geometric condition inter-dependencies, enriching semantic context through multi-view hierarchical text descriptions, and improving structural fidelity via frequency-guided loss, thereby achieving state-of-the-art realism and controllability.
SwiftNDC is a fast, general framework that employs a Neural Depth Correction field to generate cross-view consistent depth maps, providing a robust geometric initialization that significantly accelerates 3D Gaussian Splatting for high-fidelity mesh reconstruction and improves novel-view synthesis quality.
The paper proposes Quality-Aware Robust Multi-View Clustering (QARMVC), a novel framework that addresses heterogeneous observation noise by leveraging reconstruction discrepancies to generate instance-level quality scores, which then guide a hierarchical learning strategy to adaptively suppress noise and construct a robust global consensus.
This paper exposes a critical evaluation pitfall where common human preference models are biased toward large guidance scales, leading to inflated scores despite degraded image quality, and proposes a novel guidance-aware evaluation framework (GA-Eval) alongside a new method (TDG) to demonstrate that many recent diffusion guidance improvements are illusory and that simply increasing CFG scales often outperforms them in practice.
GIFSplat introduces a purely feed-forward, iterative refinement framework for 3D Gaussian Splatting from sparse unposed views that distills a frozen diffusion prior into Gaussian-level cues to achieve state-of-the-art reconstruction quality with second-scale inference time, eliminating the need for camera poses or test-time optimization.
This paper introduces Causal Motion Diffusion Models (CMDM), a unified framework that combines a Motion-Language-Aligned Causal VAE with an autoregressive diffusion transformer to achieve high-quality, real-time, and temporally consistent motion generation while overcoming the limitations of existing bidirectional and unstable autoregressive approaches.
BetterScene enhances novel view synthesis for sparse, unconstrained real-world photos by integrating a feed-forward 3D Gaussian Splatting model with a Stable Video Diffusion backbone that is fine-tuned via temporal equivariance regularization and vision foundation model-aligned representations within its VAE module to produce consistent, artifact-free views.
This paper introduces -DPO, a novel Fairness Direct Preference Optimization framework for Large Multimodal Models that mitigates both catastrophic forgetting and data imbalance-induced bias through a new loss function and pairwise preference alignment, achieving state-of-the-art performance in continual learning benchmarks.
The paper presents LoR-LUT, a unified low-rank formulation that generates compact, interpretable 3D lookup tables by combining basis LUTs with low-rank residual corrections to achieve expert-level image retouching with high perceptual fidelity and a sub-megabyte model size.
This paper proposes DP-aware AdaLN-Zero, a sensitivity-aware conditioning mechanism for diffusion transformers that mitigates heavy-tailed gradients caused by heterogeneous contexts, thereby reducing clipping bias and improving utility in differentially private time-series tasks without compromising standard performance.
The paper introduces SATtxt, a vision-language foundation model that distills multi-spectral knowledge into an RGB-only inference framework aligned with instruction-augmented LLMs, thereby achieving superior zero-shot classification, retrieval, and linear probing performance on satellite imagery benchmarks.
This paper presents Coded-E2LF, a purely event-based computational imaging method that utilizes a coded aperture to reconstruct 4-D light fields with pixel-level accuracy from a stationary event-only camera, marking the first demonstration of achieving this without relying on intensity images.
This paper introduces CGSA, a novel source-free domain adaptive object detection framework that integrates object-centric learning with a DETR-based detector by employing hierarchical slot awareness and class-guided contrastive learning to achieve superior cross-domain adaptation without requiring source data.
This paper proposes a novel framework for instruction-based image editing that bridges understanding and generation by leveraging a multi-modality chain-of-thought approach to separately handle planning, editing region reasoning, and hint-guided generation, thereby achieving superior performance on complex real-world images compared to prior single-modality methods.
The paper proposes CRAG, a novel framework that reformulates 3D assembly as a joint generation and pose estimation task to simultaneously synthesize missing geometry and predict part poses, thereby achieving state-of-the-art performance on complex, incomplete objects by leveraging the mutual reinforcement between structural reasoning and holistic shape inference.
This paper challenges the notion that quadrifocal tensors are impractical by introducing a novel synchronization framework based on Tucker decomposition and joint optimization with lower-order tensors, enabling the effective recovery of multiple camera views from higher-order geometric constraints.
This paper proposes a plug-and-play Multimodal Weight Allocation Module (MWAM) that utilizes a Frequency Ratio Metric to quantify and dynamically rebalance modality contributions during training, thereby mitigating catastrophic performance degradation caused by missing modalities and enhancing robustness across diverse multimodal architectures.
This paper introduces Interactive Medical-SAM2 GUI, an open-source, Napari-based desktop application that streamlines semi-automatic 3D medical image annotation by integrating SAM2-style mask propagation with interactive box and point prompting to enable efficient, cohort-oriented workflows for research.
This paper addresses the limitations of existing small-scale audio-visual quality assessment datasets by proposing a practical crowdsourcing framework and systematic data preparation strategy to construct YT-NTU-AVQ, the largest and most diverse AVQA dataset to date, which features 1,620 user-generated sequences with rich annotations for multimodal perception research.
This paper introduces a novel framework for monocular open-vocabulary 3D occupancy prediction in indoor scenes that leverages geometry-only supervision and 3D Language-Embedded Gaussians, enhanced by an opacity-aware Poisson-based aggregation operator and a progressive temperature decay schedule to overcome feature mixing and convergence challenges, thereby achieving state-of-the-art performance on the Occ-ScanNet benchmark.