CineMesh4D reconstructs personalized 4D whole-heart meshes directly from multi-view 2D cine MRI via cross-domain mapping with differentiable rendering and dual-context temporal blocks.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
PoseShield learns a neural collision field directly in SMPL pose space with Eikonal regularization to correct self-collisions post-hoc in human pose estimation and motion generation, achieving 95.8% success on a new benchmark.
IMU-to-4D uses wearable IMU data and repurposed LLMs to predict coherent 4D human motion plus coarse scene structure, outperforming cascaded state-of-the-art pipelines in temporal stability.
citing papers explorer
-
CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI
CineMesh4D reconstructs personalized 4D whole-heart meshes directly from multi-view 2D cine MRI via cross-domain mapping with differentiable rendering and dual-context temporal blocks.
-
PoseShield: Neural Collision Fields for Human Self-Collision Resolution
PoseShield learns a neural collision field directly in SMPL pose space with Eikonal regularization to correct self-collisions post-hoc in human pose estimation and motion generation, achieving 95.8% success on a new benchmark.
-
Seeing Without Eyes: 4D Human-Scene Understanding from Wearable IMUs
IMU-to-4D uses wearable IMU data and repurposed LLMs to predict coherent 4D human motion plus coarse scene structure, outperforming cascaded state-of-the-art pipelines in temporal stability.