RoboWM-Bench evaluates video world models by converting their manipulation video predictions into executable actions validated in simulation, showing that visual plausibility does not guarantee physical executability.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
8 Pith papers cite this work. Polarity classification is still indexing.
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2026 8verdicts
UNVERDICTED 8representative citing papers
OmniCamera disentangles video content and camera motion for multi-task generation with arbitrary camera control via the OmniCAM hybrid dataset and Dual-level Curriculum Co-Training.
A retrieval-based framework using foundation models for pose-aware correspondence to estimate population-level wildlife body proportions from unconstrained images, with reported 10-20% median relative errors on bird and amphibian datasets.
CoGE achieves state-of-the-art monocular geometric estimation in colonoscopy by training solely on simulated data via an illumination-aware Retinex-based module and a wavelet-based structure-aware module.
SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
A dual-tower 4D embodied world model called RoboStereo reduces geometric hallucinations and delivers over 97% relative improvement on manipulation tasks via test-time augmentation, imitative learning, and open exploration.
FoundDP integrates DP-derived metric depth with ViT-based structural priors from monocular models, using feature alignment to mitigate defocus blur and improve depth in low-observability areas.
GeoFuse fuses aligned road maps with satellite imagery via token/channel interactions and dynamic gating plus contrastive learning, lifting Recall@1 by 3.46% on University-1652 and 23.18% on DenseUAV under weather degradation.
citing papers explorer
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RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation
RoboWM-Bench evaluates video world models by converting their manipulation video predictions into executable actions validated in simulation, showing that visual plausibility does not guarantee physical executability.
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OmniCamera: A Unified Framework for Multi-task Video Generation with Arbitrary Camera Control
OmniCamera disentangles video content and camera motion for multi-task generation with arbitrary camera control via the OmniCAM hybrid dataset and Dual-level Curriculum Co-Training.
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WildProp: Visual Estimation of Wildlife Body Proportions at Scale
A retrieval-based framework using foundation models for pose-aware correspondence to estimate population-level wildlife body proportions from unconstrained images, with reported 10-20% median relative errors on bird and amphibian datasets.
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CoGE: Sim-to-Real Online Geometric Estimation for Monocular Colonoscopy
CoGE achieves state-of-the-art monocular geometric estimation in colonoscopy by training solely on simulated data via an illumination-aware Retinex-based module and a wavelet-based structure-aware module.
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SS3D: End2End Self-Supervised 3D from Web Videos
SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
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RoboStereo: Dual-Tower 4D Embodied World Models for Unified Policy Optimization
A dual-tower 4D embodied world model called RoboStereo reduces geometric hallucinations and delivers over 97% relative improvement on manipulation tasks via test-time augmentation, imitative learning, and open exploration.
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FoundDP: Revisiting Weak Disparity Observability in Dual-Pixel Depth Estimation
FoundDP integrates DP-derived metric depth with ViT-based structural priors from monocular models, using feature alignment to mitigate defocus blur and improve depth in low-observability areas.
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Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse
GeoFuse fuses aligned road maps with satellite imagery via token/channel interactions and dynamic gating plus contrastive learning, lifting Recall@1 by 3.46% on University-1652 and 23.18% on DenseUAV under weather degradation.