multi-view

DR-MV3D presents a map-grounded learning framework with dense rewards to improve multi-view 3D visual question answering through global map construction, view-trajectory planning, and egocentric grounding.

PAIWorld enhances diffusion-transformer world models with geometric awareness and cross-view attention to improve multi-view 3D consistency for robotic manipulation tasks, achieving state-of-the-art results on benchmarks.

This paper introduces a non-parametric multi-view Gaussian process framework for detecting machine-generated text that is robust to adversarial manipulations like paraphrasing. By combining complementary features and providing calibrated uncertainty, it outperforms existing detectors on held-out attacks.

WEAVER is a multi-view world model for robotic manipulation that achieves high fidelity, consistency, and efficiency using flow-matching loss, demonstrating superior performance in policy evaluation, improvement, and test-time planning with significant real-world improvements.

X-Stream introduces the first benchmark for multi-stream video understanding, evaluating MLLMs as multiplexers across multiple concurrent streams. The study reveals that current MLLMs achieve only about 50% accuracy, exposing significant limitations in handling multiple streams.

PaGeR adapts the multi-view perspective foundation model Depth Anything 3 to predict scale-invariant and metric depth, surface normals, and sky segmentation from a single equirectangular image, using a fixed cubemap representation that keeps VRAM and runtime constant. The paper also releases the ZüriPano and PanoInfinigen datasets.

Introduces GARD, a diffusion-based framework that operates in the feature space of a feed-forward 3D reconstructor to jointly recover scene geometry and high-quality imagery from degraded inputs.

GenRecon introduces a method for 3D scene reconstruction that integrates generative 3D priors with multi-view image conditioning, achieving high-fidelity, editable mesh reconstructions of indoor environments and outperforming existing methods by 16%.

This paper introduces three parameter-efficient methods for multi-view proficiency estimation on the Ego-Exo4D dataset, shifting from discriminative classification to generative feedback. The proposed models achieve state-of-the-art accuracy with significantly fewer parameters and training epochs than video-transformer baselines.

MultiWorld is a unified framework for multi-agent multi-view video world modeling that achieves accurate control of multiple agents while maintaining multi-view consistency through a Multi-Agent Condition Module and Global State Encoder.