arXiv Computer Vision
CapFrame: Text-Instructed Viewpoint Grounding in 3D Gaussian Scenes via Geometric Pseudo Labels
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Aug 27
PIVOT: A Multi-Trajectory Dataset and Testbed for Pose, Intrinsics, and Novel Viewpoint Evaluation in Real-World 3D Reconstruction
PIVOT is a new multi‑trajectory dataset and evaluation framework that captures real‑world scenes with diverse camera paths, preserving both sensor‑derived measured poses and COLMAP‑optimized poses along with calibrated and optimized intrinsics. It defines three benchmark families—seen vs. unseen trajectory generalization, measured vs. optimized pose sensitivity, and calibrated vs. optimized intrinsics sensitivity—and introduces a directed pose‑space Chamfer distance to assess pose coverage. The first version of PIVOT includes five scenes recorded with a DJI Mini 4 Pro and offers an open processing and Nerfstudio‑based evaluation toolchain, revealing a consistent quality gap between held‑out and unseen trajectories and significant sensitivity to pose source and camera intrinsics.
Hugging Face Trending Papers
Jul 6
From Fixed to Free Cameras: Calibration-Free View-Robust Vision-Language-Action Model
Real-world robot deployment rarely maintains the training-stage camera setup, where cameras often experience repositioning or remounting depending on actual scenarios. Existing view-robust Vision-Language-Action (VLA) policies tolerate such camera variations only when the camera extrinsics are explicitly provided, making them fragile and hard to use especially when view robustness is critical.
arXiv AI
Jul 7
From Fixed to Free Cameras: Calibration-Free View-Robust Vision-Language-Action Model
arXiv:2607. 05396v1 Announce Type: cross Abstract: Real-world robot deployment rarely maintains the training-stage camera setup, where cameras often experience repositioning or remounting depending on actual scenarios.
arXiv AI
Jun 24
OrbitForge: Text-to-3D Scene Generation via Reconstruction-Anchored Video Synthesis
arXiv:2606. 24799v1 Announce Type: cross Abstract: Generic text-to-video models can be used as rich open-world scene priors.
arXiv Machine Learning
Jul 8
PoseVLA: Universal Pose Pretraining for Generalizable Vision-Language-Action Policies
arXiv:2602. 19710v3 Announce Type: replace-cross Abstract: Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level perception with sparse, embodiment-specific action supervision.
arXiv AI
Aug 20
GS-VLA: Plug-and-Play Viewpoint Canonicalization for Frozen VLA Policies via Gaussian Splatting
GS‑VLA introduces a lightweight, plug‑and‑play framework that uses a 4 M‑parameter 3D‑Gaussian canonicalizer to adapt frozen Vision‑Language‑Action (VLA) policies to viewpoint shifts without retraining the policy. By treating viewpoint changes as a localized novel‑view synthesis problem under a locality assumption, the method normalizes observations through a scene‑ and policy‑independent disocclusion task. Experiments on the LIBERO benchmark demonstrate that GS‑VLA recovers a large portion of performance lost due to camera displacement, improving results across different policy architectures, unseen task suites, and perturbation scales. whyItMatters":"The approach offers a computationally efficient alternative to costly fine‑tuning or generative augmentation, enabling robust VLA deployment in real‑world settings where camera configurations may vary."